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Applied Epid

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PrevMed Class of Dr. Chan

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• Qualitative diagnostic test Quantitative diagnostic test 1. Normal distribution (Gaussian) Curve 2. Percentile method 3. Therapeutic method 4. Diagnostic or Predictive value method
• At the beginning of 1992, there are 4 cases, prevalence is 4/100; at the beginning of 1993, the prevalence is 5/100; 7/100 in 1994 and 5/100 in 1995 Incidence rate, we consider only the 96 individuals free of the disease at the beginning of 1992; 5 new cases in 1992; 6 new cases in 1993; 5 new cases in 1994; The 3-year incidence of the disease 16/96; but the annual incidence is 5/96 in 1992; 6/91 in 1993; and 5/85 in 1994
• Incidence rate is 3/33 person-years or 9.1 cases per 100 person-years; cumulative incidence is 3/7 or 43 case per 100 persons; the average duration of disease is 10/3 or 3.3 years Prevalence at year 4 = 2/6 or 33 cases per 100 persons but the average prevalence is duration of disease x incidence rate = 3.3 X 9.1 = 30 cases per 100 population
• Predictive value method
• Receiver operator characteristic curve Tests that discriminate well crowd toward the upper left corner of the ROC curve;Tests that perform less well have curves that fall closer to the diagonal running from left lower to upper right. The diagonal line shows the relationship between true-positive and false positive rates that would occur for a test yielding no information.
• Likelihood ratio for hypothyroidism were highest for low levels of T4 and lowest for high levels. The lowest values in the distribution of T4 (&lt;4.0 mg/dL) were only seen in patients with hypothyroidism (these levels ruled in the diagnosis). The highest levels (&gt;8.) mg/dL) were not seen in patients with hypothyroidism (the presence of these levels ruled out the disease)
• An instrument can be valid (accurate) on the average but not be reliable; because the measures obtained are widely scattered about the true value. On the otherhand, an instrument can be very reliable but be systematically off the mark (inaccurate); A single measurement with poor reliability has low validity because it is likely to be off the mark simply because of chance alone.
• Another assumption underlies attempts at early diagnosis. This element was described by Hutchison in 1960 and consists of a “critical point” in the natural history of a disease, before which therapy is either more effective or easier to apply than afterward. A disease may have several critical points (pulmonary tuberculosis) or may have none (several cancers), and the location of these critical points along its natural history is crucial to the value of early diagnosis.
• No benefit could be confirmed among women under age 50, but striking reductions in breast cancer mortality were observed at age 50 and beyond (the mortality from other causes of death was identical, confirming that randomization had produced comparable groups of experimental and control women). This landmark randomized trial (confirmed by additional subsequent trials) demonstrated that a critical point does, in fact, exist in the natural history of breast cancer and that it is located between the point where early diagnosis is possible and the time of usual clinical diagnosis.
• Observational studies allow nature to take its course: the investigator measures but does not intervene. In an experiment the investigator studies the impact of varying some factor that he controls. For example, he may take a litter of rats, expose one of two randomly selected halves to a supposedly carcinogenic agent, and then record the frequency with which cancer develops in the two groups. In the more usual approach the investigator can only observe the occurrence of disease in people who are already segregated into groups on the basis of some experience or exposure. In this kind of study, allocation into groups on the basis of exposure to a factor is not under the control of the investigator.
• An ecological fallacy results if inappropriate conclusions are drawn on the basis of ecological data. The association observed between variables at the group level does not necessarily represent the association that exists at the individual level
• Uncertainty about the temporal sequence and biases associated with the study of cases of longer duration (old cases) Clinicians use incidence and prevalence for predicting future course of the disease, assigning a probability to a patient, and making comparisons. Clinicians use measures of frequency as the ingredients in comparative measures of the association between a factor and the disease or disease outcome.
• Odds ratio is the ratio of the odds of exposure among cases to the odds in favor of exposure among the controls.
• Attributable risk refers to the magnitude of disease attributable to a risk factor
• Relative risk of a disease is the ratio of incidence in exposed persons to incidence in non-exposed persons
• Selection bias occurs when there is a systematic difference between the characteristics of the people selected for a study and the characteristics of those who are not. (when participants select themselves for a study, either because they are unwell or because they are particularly worried about an exposure.) Confounding can occur when another exposure exists in the study population and is associated both with the disease and the exposure being studied. Example : Coffee drinking, cigarette smoking, and coronary heart disease.
• Best case/worst case analysis – A cohort of 123 morbidly obese patients was studied 19-47 months after surgery. Success was defined as having lost more than 30% of excess weight. Only 103 patients (84%) could be located. In these, the success rate of surgery was 60/103 (58%). Best case success rate (60+20)/123 or 65%; Worse case success rate 60/123 or 49% Thus the true rate must have been between 49 and 65%.
• A schematic diagram of sufficient causes in a hypothetical individual. Each constellation of component causes is minimally sufficient to produce disease; that is, there is no redundant or extraneous component cause – each one is a necessary part of that specific causal mechanism. A specific component cause may play a role in one, several, or all of the causal mechanism. It can facilitate an understanding of some key concepts such as 1. strength of effect 2 interaction
• When more than one cause act together, the resulting risk may be greater than or less than would be expected by simply combining the effects of the separate causes Effect modification is present when the strength of the relationship between two variables is different according to the level of some third variable, called an effect modifier. Thiazide diuretics at 25, 50, 100 mg – sudden death – potassium sparing therapy
• It is not difficult to appreciate the relationship between exposure and disease for conditions such as chicken pox, sunburn, and aspirin overdose,
• I p = Incidence rate of the disease in the total population; I u = Incidence rate of the disease among the unexposed group
• A risk factor that is not a cause of disease is called marker because it “marks” the increased probability of disease Knowledge of risk can be used in the diagnostic process, since the presence of a risk factor increased the prevalence of disease among patients – one way of improving the positive predictive value of a diagnostic test. If a risk factor is also a cause of disease, its removal can be used to prevent disease whether or not the mechanism by which the disease takes place is known.
• Efficacious treatment is one that has the desired effects among those who receive it. Effective if it does more good than harm in those to whom it is offered.
• Experimental studies in medicine that involve humans are called clinical trials Controlled trials are studies in which the experimental drug or procedure is compared with another drug or procedure, sometimes a placebo and sometimes the previously accepted treatment Uncontrolled trials are studies in which the investigator’s experience with the experimental drug or procedure is described, but the treatment is not compared with another drug Concurrent control is the control that is given intervention for the same period of time as the study group
• Phase 3 performed in a larger and more heterogeneous population than in phase 2
• Prognostic staging of AIDS – once patients with HIV infection develop AIDS, the prognosis is poor and survival time is short.- with 1 point for the presence of each of 7 factors – severe diarrhea or a serum albumin &lt;2.0 gm/dL, any neurologic deficit, P o2 less than or equal to 50 mm Hg, hematocirti &lt;30%. ;lymphocyte count &lt;150/mL, white count &lt;2500/mL, and platelet count &lt;140,000/mL – Stage I, 0 point; II, 1 point; III, greater than or equal to 2 points
• Aware of the benign clinical course of such lumps and alert to the potential dangers of labeling the patient as having a “tumor” you probably will decide to tell him nothing, at least until his current problem is resolved and simply will make a note to check the lipoma at a subsequent visit to confirm its innocence. 2. Aware of the serious prognosis and alert to the potential benefit of prompt surgical evaluation, you will inform the patient of her condition and arrange an early referral.
• In most cases, the effect would be to make prognosis appear gloomier than it really is. However, distortion in the opposite direction also can occur
• Several studies of the risk of stone recurrence ask currently symptomatic patients if they have had stones previously, failing to realize that recurrent stone formers (with positive past histories) have multiple chances to be included in such studies, but patients without recurrences (with negative past histories) have only one chance of being included; no wonder recurrence rates vary all over the map.
• These biases will distort the conclusions of the study
• Best case and Worse case approach
• An article about the prognosis of patients with transient ischemic attack. If the article describes the risk of “subsequent stroke” without presenting the explicit, objective critieria for what constituted a “stroke”, you are in a quandary. Are these “strokes” limited to severe derangements of sensation or motor power? Or, are the majority of these “strokes” merely transient or trivial changes in sensation or in deep or superficial reflexes? The implications of these different definitions for counseling patients or initiating therapy are whopping
• This is essential to avoid the two following biases.
• The multivariate approaches used will fail to distinguish important prognostic factors from unimportant idiosyncracies of the particular patient sample (the training sample) to which they are applied.
• Primary prevention is often accomplished outside the health care system at the community level. E.g. chlorination and fluoridation of the water supply
• Most secondary prevention is done in clinical settings A screening test is not intended to be diagnostic
• Destitution, refers to the financial cost of illness (for individual patients or society)
• Before undertaking a health promotion procedure on a patient, especially if the procedure is controversial among expert groups, the clinician should discuss both the pros (probability of and hoped for health benefits) and cons (probability of unintended effects) of the procedure with the patient
• Applied Epid

1. 1. APPLIED EPIDEMIOLOGY Prepared by Antonio E. Chan, M.D.
2. 2. Learning objectives <ul><li>Define epidemiology and outline its scope </li></ul><ul><li>Differentiate epidemiology from clinical epidemiology </li></ul><ul><li>Describe approaches to establishing “normality” </li></ul><ul><li>Describe criteria and measures of disease occurrence commonly used in epidemiology </li></ul><ul><li>Enumerate some routinely available data use in epidemiology </li></ul>
3. 3. Learning objectives <ul><li>Understand diagnostic test in relation to disease </li></ul><ul><li>Describe the main types of epidemiological studies </li></ul><ul><li>Enumerate the advantages and disadvantages of observational studies compared with experimental studies </li></ul><ul><li>Explain cause of disease </li></ul><ul><li>Outline the steps necessary to establish the cause of disease </li></ul>
4. 4. Learning objectives <ul><li>Appreciate the differing approaches used in epidemiology to compare the occurrence of disease </li></ul><ul><li>Outline the role of epidemiology in describing the natural history of a disease and prognosis </li></ul><ul><li>Understand the role of epidemiology in the prevention and control of disease through identification of the causes of disease </li></ul><ul><li>Relate the different stages of the development of a disease to the phases of prevention </li></ul>
5. 5. What is Epidemiology ? <ul><li>The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems </li></ul>
6. 6. AIMS OF EPIDEMIOLOGY <ul><li>To understand the course of the disease (natural history of the disease) </li></ul><ul><li>To identify the causes or risk factors </li></ul><ul><li>To provide effective measures of treatment and prevention </li></ul>
7. 7. <ul><li>Uses of epidemiology </li></ul><ul><li>Genetic factors </li></ul><ul><li>Causation </li></ul><ul><li>Environmental factors </li></ul><ul><li>(including lifestyle) </li></ul><ul><li>2. Natural history </li></ul><ul><li>3. Description of health status </li></ul><ul><li>of population </li></ul><ul><li>Proportion with ill health, </li></ul><ul><li>change over time, </li></ul><ul><li>change with age, etc </li></ul>Good health Ill health Good health Subclinical changes Clinical disease Death Recovery Good health ILL health Time
8. 8. <ul><li>Uses of epidemiology </li></ul><ul><li>Evaluation of </li></ul><ul><li>intervention </li></ul>Good health Ill health Treatment Medical care Health promotion Preventive measures Public health services
9. 9. APPLIED EPIDEMIOLOGY <ul><li>Clinical epidemiology </li></ul><ul><li>Communicable disease epidemiology </li></ul><ul><li>Environmental and occupational epidemiology </li></ul><ul><li>Molecular epidemiology </li></ul>
10. 10. CLINICAL EPIDEMIOLOGY <ul><li>Definition </li></ul><ul><li>is the application of epidemiological principles and methods to the practice of clinical medicine </li></ul><ul><li>is the science of making predictions about individual patients by counting clinical events in similar patients, using scientific methods for studies of groups of patients to ensure that the predictions are accurate </li></ul>
11. 11. CLINICAL EPIDEMIOLOGY <ul><li>Purpose: </li></ul><ul><li>to develop and apply methods of clinical observations that will lead to valid conclusions by avoiding being misled by systematic error and chance </li></ul><ul><li>to make good decisions in the care of patients </li></ul>
12. 12. The Relationship Between EPIDEMIOLOGY + CLINICAL MEDICINE Populations Individuals <ul><li>Studies/Assessments </li></ul><ul><li>Prevention </li></ul><ul><li>Evaluation </li></ul><ul><li>Planning </li></ul><ul><li>Diagnosis </li></ul><ul><li>Treatment </li></ul><ul><li>Curing </li></ul><ul><li>Caring </li></ul>
13. 13. Clinical Question Issue Question Abnormality Is the patient sick or well ? Diagnosis How accurate are tests used to diagnose disease ? Frequency How often does a disease occur ? Risk What factors are associated with an increased risk of disease ? Prognosis What are the consequences of having a disease ? Treatment How does treatment change the course of disease ? Prevention Does an intervention on well people keep disease from arising ? Does early detection and treatment improve the course of disease ? Cause What conditions lead to disease ? What are the pathogenetic mechanisms of disease Cost How much will care for an illness cost ?
14. 14. Sources of data useful for epidemiology studies <ul><li>Data on vital events – birth and death </li></ul><ul><li>Morbidity or disease statistics </li></ul><ul><li>Data on physiologic and or pathologic condition </li></ul><ul><li>Statistics on health resources and services </li></ul><ul><li>Statistics pertaining to the environment </li></ul><ul><li>Demographic data </li></ul><ul><li>Socio-cultural data </li></ul>
15. 15. Measuring Health and Disease Clinical question: Is the patient sick or well? <ul><li>Health is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” </li></ul><ul><li>Epidemiologist’s definition of health states </li></ul><ul><li>“ disease present” or “disease absent” </li></ul>
16. 16. Measuring Health and Disease Clinical question: Is the patient sick or well? <ul><li>Diagnostic tests </li></ul><ul><li>qualitative diagnostic test </li></ul><ul><li>quantitative diagnostic test </li></ul><ul><li>Normal (Gaussian) distribution method </li></ul><ul><li>Percentile method </li></ul><ul><li>Therapeutic method </li></ul><ul><li>Predictive value method </li></ul>
17. 17. Measuring Health and Disease <ul><li>Diagnostic criteria are usually based on </li></ul><ul><li>symptoms, signs and test results </li></ul><ul><li>1. Hepatitis presence of antibodies in the blood </li></ul><ul><li>2. Asbestosis - symptoms and signs of specific changes </li></ul><ul><li>in lung function, </li></ul><ul><li>- radiographic demonstration of fibrosis of </li></ul><ul><li>the lung tissue or pleural thickening and </li></ul><ul><li>- history of exposure to asbestos fibers. </li></ul>
18. 18. <ul><li>Major Manifestations Minor Manifestations </li></ul><ul><li>Carditis Clinical: </li></ul><ul><li>Polyarthritis fever </li></ul><ul><li>Chorea athralgia (joint pains) </li></ul><ul><li>Erythema marginatum previous rheumatic fever or </li></ul><ul><li>Subcutaneous nodules rheumatic heart disease </li></ul><ul><li>Laboratory: </li></ul><ul><li>Acute phase reactants: </li></ul><ul><li>Abnormal ESR, CRP, </li></ul><ul><li>leukocytosis </li></ul><ul><li>Prolonged P-R interval </li></ul>The Jones Criteria (revised) for Guidance in the Diagnosis of Acute Rheumatic Fever A high probability of rheumatic fever is indicated by the presence of two major or one major and two minor, manifestations, if supported by evidence of a preceding Group A streptococcal infection
19. 19. MAJOR SIGNS MINOR SIGNS Weight loss > 10% Persistent cough > 1 month Fever > 1 month General pruritic dermatitis Chronic diarrhea > 1 month Recurrent herpes zoster General lymphadenopathy Chronic herpes simplex Oral candidiasis WHO CASE-DEFINITION FOR AIDS The presence of disseminated Kaposis sarcoma or cryptococcal meningitis or Two major signs in association with at least one minor sign
20. 20. Measuring Health and Disease <ul><li>Diagnostic criteria must be clearly stated, easy to use and easy to measure in a standard manner under a wide variety of circumstances by different people </li></ul><ul><li>Diagnostic criteria may change quite rapidly as knowledge or techniques improve. </li></ul><ul><li>Definitions used in clinical practice are less rigidly specified and clinical judgment is more important in diagnosis </li></ul>
21. 21. Measuring Health and Disease <ul><li>The development of criteria to establish the presence of disease requires definition of normality and abnormality </li></ul><ul><li>Difficult to define what is normal </li></ul><ul><li>No clear distinction between normal and abnormal </li></ul>
22. 22. Approaches in establishing “normality” Clinical question: Is the patient sick or well ? <ul><li>Problem (misclassification) </li></ul><ul><li>Clinical measurements </li></ul><ul><li>nominal asymptomatic </li></ul><ul><li>ordinal cut-off point </li></ul><ul><li>interval or ratio </li></ul><ul><li>Clinical measurements have skewed distributions </li></ul><ul><li>Percentile method ( same prevalence rates) </li></ul>
23. 23. Level at which treatment does more good than harm - Cost In specific age groups for men and women at which treatment makes economic as well as medical sense Criteria change from time to time
24. 27. Level at which treatment does more good than harm - Cost In specific age groups for men and women at which treatment makes economic as well as medical sense Criteria change from time to time
25. 28. Approaches in establishing “normality” Clinical question: Is the patient sick or well ? <ul><li>Normal Abnormal </li></ul><ul><li>common or usual being unusual </li></ul><ul><li>well being sick </li></ul><ul><li>not being treatable being treatable </li></ul>
26. 29. Measures of disease frequency Clinical question: How often does a disease occur ? <ul><li>Prevalence of a disease is the number of cases in a defined population at a specified point in time </li></ul><ul><ul><li>Point prevalence </li></ul></ul><ul><ul><li>Period prevalence </li></ul></ul><ul><li>Incidence is the number of new cases arising in a given period in a specified population </li></ul><ul><li> </li></ul>
27. 30. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>The prevalence rate (P) for a disease is calculated as follows: </li></ul><ul><li>Number of people with the disease or condition </li></ul><ul><li>P = ----------------------------------------------------------------- (x factor) </li></ul><ul><li>Number of people in the population at risk at the </li></ul><ul><li>specified time </li></ul>
28. 31. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Incidence rate (I) </li></ul><ul><li>Number of people who get a </li></ul><ul><li>disease in a specified period </li></ul><ul><li>I = ---------------------------------------------------- X (factor) </li></ul><ul><li>Sum of the length of time during which </li></ul><ul><li>each person in the population is at risk </li></ul>
29. 32. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Incidence rate </li></ul><ul><li>The numerator is the number of new events that occur in a defined time period </li></ul><ul><li>The denominator is the population at risk of experiencing the event during this period </li></ul><ul><li>The most accurate way of calculating incidence rate is to calculate the person-time incidence rate ( Incidence density ) </li></ul>
30. 33. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Cumulative incidence rate or risk (CI) </li></ul><ul><li>Number of people who get a </li></ul><ul><li>disease during a specified period </li></ul><ul><li>CI = ---------------------------------------------------- X (factor) </li></ul><ul><li>Number of people free of the disease in </li></ul><ul><li>the population at risk at the beginning of </li></ul><ul><li>the period </li></ul>
31. 36. Factors influencing observed prevalence rate Increased by: Decreased by: Longer duration of the disease Shorter duration of disease Prolongation of life of patient High case-fatality rate from disease without cure Increase in new case Decrease in new cases (increase in incidence) (decrease in incidence) In-migration of cases In-migration of healthy people Out-migration of healthy people Out-migration of cases In-migration of susceptible people Improved cure rate of cases Improved diagnostic facilities (better reporting)
32. 37. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Prevalence studies do not usually provide strong evidence of causality </li></ul><ul><li>It is helpful in assessing the need for health care and the planning of health services </li></ul><ul><li>Prevalence rates are often used to measure the occurrence of conditions for which the onset of disease may be gradual </li></ul>
33. 38. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Cumulative incidence rate </li></ul><ul><li>Unlike incidence rate, it measures the denominator only at the beginning of a study </li></ul><ul><li>This rate has a simplicity that makes it suitable for the communication of health information to decision makers </li></ul><ul><li>Easy to interpret and provide a useful summary measure </li></ul><ul><li>It is useful approximation of incidence rate when the rate is low or when the study period is short </li></ul>
34. 39. 274 CI = ------------ x 1000 = 2.3 per 1000 118,539 Example Relationship between cigarette smoking and incidence rate Stroke in a cohort of 118,539 women Never smoked 70 395,594 17.7 Ex-smoker 65 232,712 27.9 Smoker 139 280,141 49.6 Total 274 908,447 30.2 Person-years Stroke incidence rate Smoking No. of cases of observation (per 100,000 Category of stroke (over 8 years) person-years)
35. 40. Measuring disease frequency Clinical question: How often does a disease occur ? <ul><li>Case-fatality rate </li></ul><ul><li>a measure of the severity of a disease </li></ul><ul><li>No. of deaths from a disease </li></ul><ul><li>in a specified period </li></ul><ul><li>Case fatality rate = ------------------------------------------ X 100 </li></ul><ul><li>(CFR) No. of diagnosed cases of the </li></ul><ul><li>disease in the same period </li></ul>
36. 41. USE OF AVAILABLE INFORMATION (Mortality) <ul><li>Number of deaths in a specified period </li></ul><ul><li>Crude mortality rate = --------------------------------------------------------- X F </li></ul><ul><li>(CMR) Average total population during that period </li></ul><ul><li>This mortality can be made specific as to age, sex or cause </li></ul><ul><li>Not appropriate to use for comparison because death varies </li></ul><ul><li>according age, sex, race, socio-economic class and other factors </li></ul><ul><li>Comparison of mortality rates between groups of diverse age </li></ul><ul><li>structure are usually based on age-standardized rates </li></ul>
37. 42. Standardization of rates (Adjustment of rates) 1. Direct adjustment of rates This requires the selection of some population, called a standard population , to which the age-specific rates for each population can be applied. 2. Indirect adjustment of rates Standardization is based on age-specific rates rather than age composition The population whose rates form the basis for comparison is referred to as the “standard population” The larger of the two populations is usually chosen as standard because its rates tend to be more stable
38. 43. Standardization of rates (Adjustment of rates) <ul><li>If developed and an undeveloped country are compared, the developed country would probably be taken as the standard </li></ul><ul><li>A common way of carrying out indirect age-adjustment is to relate the total expected deaths thus obtained to observed deaths through a formula known as the Standardized Mortality Ratio (SMR) </li></ul><ul><li>Total observed deaths in a population </li></ul><ul><li>SMR = ------------------------------------------------------- </li></ul><ul><li>Total expected deaths in that population </li></ul>
39. 44. Standardization of rates (Adjustment of rates) <ul><li>Interpretation : </li></ul><ul><li>If this mortality ratio is greater than 1, it means that more deaths are observed in the smaller or comparison population than would be expected on the basis of rates in the larger (standard) population </li></ul><ul><li>If the ratio is less than 1, fewer deaths are observed than expected </li></ul>
40. 45. Example: Direct method Comparison of death rates in two populations by age Annual Annual Age-specific Number Crude Age Population Death rate of Death rate (years) Number Proportion (per 1000) Deaths (per 1000) (1) (2) (3) (4) (5) (6) Population A < 15 1,500 0.30 2 3 15 – 44 2,000 0.40 6 12 ≥ 45 1,500 0.30 20 30 45 All ages 5,000 1.00 45 --------- = 9.0 5,000 Population B < 15 2,000 0.40 2 4 15 – 44 2,500 0.50 6 15 ≥ 45 500 0.10 20 10 29 All ages 5,000 1.00 29 -------- = 5.8 5,000
41. 46. Computation of Expected Number of Deaths by Direct Method Example 1 : Identical Age-specific Rates Population A Population B Age-specific Age-specific Age Standard Population Death Rate Expected Death Rate Expected (years) (A and B Combined) per 1000 Deaths per 1000 Deaths (1) (2) (3)=(2)x(1) (4) (5)=(4)x(1) < 15 3,500 2 7 2 7 15 – 44 4,500 6 27 6 27 ≥ 45 2,000 20 4 0 20 40 All ages 10,000 74 74 Conclusion : There is truly no difference between A and B in risk of death
42. 47. Computation of Expected Number of Deaths by Direct Method Example 2 : Different Age-specific Rates Population A Population B Age-specific Age-specific Age Standard Population Death Rate Expected Death Rate Expected (years) (A and B Combined) per 1000 Deaths per 1000 Deaths < 15 3,500 2 7 2 7 15 – 44 4,500 6 27 10 45 ≥ 45 2,000 20 40 20 40 All ages 10,000 74 92 74 92 ---------- = 7.4 ---------- = 9.2 10,000 10,000 Conclusion : There is difference between A and B in risk of death
43. 48. Example of Indirect Method Deaths by Age and Photofluorogram Reading (Whites) for Three-and-a-Half Year Observation Period, Muscogee County, Georgia, 1946 Negative for Cardiovascular Disease Suspect for Cardiovascular Age-specific Disease Age in 1946 Number of death rates Number of (years) Population Deaths per 100 Population Deaths 15 – 34 13,681 35 0.25 23 1 35 – 54 8,838 102 1.15 24 5 55 and over 2,253 149 6.61 65 14 ---------- ------- ------- ----- All ages 24,772 286 112 20 Crude death rate per 100 1.15 17.9
44. 49. Percentage Distribution by Age of Negatives and Suspects, Muscogee County, Georgia 15 – 34 13,681 55.2 23 20.5 35 – 54 8,838 35.7 24 21.4 55 and over 2,253 9.1 65 58.0 All ages 24,772 100.0 112 99.9 Negative for Suspect for Cardiovascular Disease Cardiovascular Disease Age Percentage Percentage (years) Number of Population Number of Population
45. 50. Calculation of Standardized Mortality Ratio for Suspects Compared with Negatives, Muscogee County, Georgia (1) (2) (3) = (1) x (2) (4) 15 – 34 23 0.25 .1 1 35 – 54 24 1.15 .3 5 55 and over 65 6.61 4.3 14 All ages 4.7 20 Death Rates per 100 Expected Deaths Observed for Persons Negative among “Suspects” Deaths Age Number of for Cardiovascular According to Rates among (years) “Suspects” Disease for Negatives “Suspects” Observed deaths 20 SMR = -------------------------- = --------- = 4.25 Expected deaths 4.7
46. 51. No. of deaths in a year of children less than 1 year of age Infant mortality rate = ------------------------------------------------------ X F No. of live births in the same year A measure of overall health status for a given population It is based on the assumption that it is particularly sensitive to socio-economic changes and to health care intervention Other measures of mortality in early childhood are : 1. Fetal death rate 2. Stillbirth or late fetal death rate 3. Perinatal mortality rate 4. Neonatal mortality rate 5. Postneonatal mortality rate Mortality
47. 52. Child mortality rate is based on deaths of children aged 1 – 4 years and is important because accidental injuries, malnutrition and infectious diseases are common in this age group Maternal pregnancy-related deaths in a year Maternal mortality rate = ------------------------------------- Total births in the same year Life expectancy is the average number of years an individual of a given age is expected to live if current mortality rates continue Mortality
48. 53. Life Expectancy (years) at selected ages for four countries <ul><li>Age Mauritius Bulgaria USA Japan </li></ul><ul><li>Birth 65.0 68.3 71.6 75.8 </li></ul><ul><li>45 years 25.3 27.3 30.4 32.9 </li></ul><ul><li>65 years 11.7 12.6 15.0 16.2 </li></ul>
49. 54. DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ? Diagnostic test – the objective is to diagnose any treatable disease present Characteristics of a diagnostic test Reliable – gives the same measurement when repeated more than once Valid - measures what it intends to measure Accurate – correctly determines those with disease and those without Easy to use – can be performed by other people without difficulty Not expensive – affordable Safe and acceptable
50. 55. <ul><li>Gold standard </li></ul><ul><li>– a sounder indication of truth or a standard of accuracy </li></ul><ul><li>- a new diagnostic test is compared </li></ul><ul><li>- elusive (not available) </li></ul><ul><li>- expensive and risky – biopsy, surgical exploration, </li></ul><ul><li>autopsy </li></ul><ul><li>- sometimes simple – throat swab culture </li></ul>DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ?
51. 56. Cut-off points 80 90 100 110 120 130 140 150 160 170 Normal Group Abnormal Group B l o o d L e v e l ( mg / 100 ml )
52. 57. DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ? a + c b + d a + b + c + d a + b c + d DISEASE Present Absent TEST Positive a b Negative c d
53. 58. Validity of a diagnostic test a = no. of true positives, b = no. of false positives c = no. of false negatives, d = no. of true negatives Sensitivity = probability of a positive test in people with the disease = a/(a + c) Specificity = probability of a negative test in people without the disease Positive predictive value = probability of the person having the disease when the test is positive = a /(a + b) Negative predictive value = probability of the person not having the disease when the test is negative = d / (c + d)
54. 59. 62 87 37 112 149 Group A  -Hemolytic Streptococcus on Throat Culture Present Absent Clinical Diagnosis of Strep Pharyngitis Yes 27 35 No 10 77
55. 60. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>Use of multiple diagnostic tests </li></ul><ul><li>use of imperfect diagnostic tests, with less than </li></ul><ul><li>100% sensitivity and specificity, a single test frequently results in a probability of disease that is neither very high or very low. </li></ul>
56. 61. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>Parallel tests (all at once) </li></ul><ul><li>- used when rapid assessment is necessary as in hospitalized or emergency patients, or for ambulatory patients who cannot return easily for evaluation because they have come from a long distance </li></ul><ul><li>Parallel tests generally increase the sensitivity and, therefore, the negative predictive value for a given disease prevalence above those of each individual test. On the otherhand, specificity and positive predictive value are lowered </li></ul><ul><li>Parallel testing is useful when the clinician is faced with the need for a very sensitive test but has available only two or more relatively insensitive ones. </li></ul>
57. 62. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>Serial testing (consecutively, based on previous test result) </li></ul><ul><li>- used when rapid assessment is not required </li></ul><ul><li>- used when some of the tests are expensive or risky </li></ul><ul><li>- maximizes specificity and positive predictive value but lowers sensitivity and the negative predictive value. </li></ul><ul><li>- the process is more efficient if the test with the highest specificity is used first. </li></ul>
58. 63. Effect of Sequence is Serial Testing: A Then B versus B Then A Prevalence of Disease Number of patients tested 1000 Number of patients with disease 200 (20% prevalence) Sensitivity and Specificity of the Tests Test Sensitivity Specificity A 80 90 B 90 80 Sequence of Testing Begin with Test A Begin with Test B Disease Disease + - + - A + 160 80 240 B + 180 160 340 - 40 720 760 - 20 640 660 200 800 1000 200 800 1000 240 Patients Retested with B 340 Patients Retested with A Disease Disease + - + - B + 144 16 160 A + 144 16 160 - 16 64 80 - 46 144 180 160 80 240 180 160 340
59. 64. DISEASE Clinical question: How accurate are tests used to diagnose disease ? <ul><li>Statements about validity test </li></ul><ul><li>Sensitivity and specificity are inversely related. </li></ul><ul><li>A sensitive test can pick up most cases of the disease but it will erroneously label as positive many persons who do not have the disease. </li></ul><ul><li>A highly specific test will correctly label as negative those who do not have the disease but it will miss many cases. </li></ul>
60. 65. Trade-Off between Sensitivity and Specificity when Diagnosing Diabetes Blood Sugar Level 2 hr after Eating Sensitivity Specificity (mg/100 mL) (%) (%) 70 98.6 8.8 80 97.1 25.5 90 94.3 47.6 100 88.6 69.8 110 85.7 84.1 120 71.4 92.5 130 64.3 96.9 140 57.1 99.4 150 50.0 99.6 160 47.1 99.8 170 42.9 100.0 180 38.6 100.0 190 34.3 100.0 200 27.1 100.0
61. 66. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>A very sensitive test gives a low positive predictive value since it produces many false positive. Conversely, a very specific test gives a high positive predictive value. </li></ul><ul><li>Sensitivity and specificity are unaffected by the prevalence of the disease or condition. Since sensitivity depends only on those with the disease or condition and specificity only on those without the disease or condition. </li></ul><ul><li>The positive predictive value of a test increases with the prevalence of the disease. </li></ul>
62. 67. Positive Test 0 20 40 60 80 100 100 80 60 40 20 0 Prevalence of Disease (Percentage) Predictive value (Percentage) Negative Test
63. 70. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>Uses of sensitive tests </li></ul><ul><li>A sensitive test should be chosen when there is an important penalty for missing a disease (dangerous but treatable condition) </li></ul><ul><li>A sensitive test is most helpful to the clinician when the test result is negative (to rule out disease) </li></ul><ul><li>Uses of specific tests </li></ul><ul><li>Highly specific tests are needed when false-positive results can harm the patient physically, emotionally, or financially. </li></ul><ul><li>A specific test is most helpful when the test result is positive </li></ul><ul><li>(to confirm or “rule in” the disease) </li></ul>
64. 71. LIKELIHOOD RATIOS <ul><li>Alternative way of describing the performance of a diagnostic test </li></ul><ul><li>Summarize the same kind of information as sensitivity and specificity </li></ul><ul><li>Used to calculate the probability of disease after a positive or negative test (positive or negative predictive value) </li></ul><ul><li>Advantage – can be used at multiple level of test results. </li></ul>
65. 72. LIKELIHOOD RATIOS <ul><li>Use of likelihood ratios depends on odds </li></ul><ul><li>Probability </li></ul><ul><ul><li>Used to express sensitivity, specificity and predictive value </li></ul></ul><ul><ul><li>Is the proportion of people in whom a particular characteristic, such as a positive test, is present </li></ul></ul>
66. 73. LIKELIHOOD RATIOS <ul><li>Odds </li></ul><ul><ul><li>Is the ratio of two probabilities (the probability of an event to that of 1 – probability of event </li></ul></ul><ul><li>Odds and probability contain the same information, but they express it differently </li></ul>
67. 74. LIKELIHOOD RATIOS <ul><li>The two can be interconverted using simple formulas: </li></ul><ul><li>Probability of event </li></ul><ul><li>Odds = ------------------------------- </li></ul><ul><li>1 – Probability of event </li></ul><ul><li>Odds </li></ul><ul><li>Probability = ------------------------- </li></ul><ul><li>1 + Odds </li></ul>
68. 75. LIKELIHOOD RATIOS <ul><li>Express how many times more (or less) likely a test is to be found in diseased, compared with non-diseased, people. </li></ul><ul><li>If a test yields dichotomous results (both positive and negative) </li></ul><ul><li>Two types of likelihood ratios described its ability to discriminate between diseased and non-diseased people </li></ul>
69. 76. LIKELIHOOD RATIOS <ul><li>Test’s positive likelihood ratio (LR+) </li></ul><ul><ul><li>the ratio of the proportion of diseased people with a positive test result (sensitivity) to the proportion of non-diseased with a positive test result </li></ul></ul><ul><ul><li>(1 – specificity) </li></ul></ul><ul><li>Test’s negative likelihood ratio (LR-) </li></ul><ul><ul><li>the proportion of diseased people with a negative test result (1 – sensitivity) divided by the proportion of non-diseased people with a negative test result (specificity) </li></ul></ul>
70. 77. LIKELIHOOD RATIOS <ul><li>Example: Diagnostic Characteristics of a D-dimer Assay in Diagnosing Deep Venous Thrombosis (DVT) </li></ul>Test Disease DVT according to Gold Standard (Compression ultrasonography and /or 3 month follow up)` D-dimer Assay for Diagnosis of DVT Present Absent Total Positive 34 168 202 Negative 1 282 283 Total 35 450 485
71. 78. LIKELIHOOD RATIOS <ul><li>Sensitivity 34 / 35 </li></ul><ul><li>LR + = ----------------- = --------------- = 2.6 </li></ul><ul><li>1 – Specificity 168 / 450 </li></ul><ul><li>1 – Sensitivity 1 / 35 </li></ul><ul><li>LR - = ------------------- = ---------------- = .045 ~ .05 </li></ul><ul><li>Specificity 282 / 450 </li></ul>
72. 79. INTERPRETATION OF LIKELIHOOD RATIOS <ul><li>Likelihood Ratio is the probability of a particular test </li></ul><ul><li>result for a person with the disease of interest divided </li></ul><ul><li>by the probability of that test result for a person </li></ul><ul><li>without the disease of interest </li></ul><ul><li>An LR+ of one indicates a test with no value in sorting </li></ul><ul><li>out persons with and without the disease of interest, </li></ul><ul><li>since the probability of a positive test result is equally </li></ul><ul><li>likely for affected and unaffected persons. </li></ul>
73. 80. <ul><li>The larger the value of the LR+, the stronger the </li></ul><ul><li>association between having a positive test result and </li></ul><ul><li>having the disease of interest </li></ul><ul><li>The larger the size of the LR+ the better the diagnostic </li></ul><ul><li>value of the test. Although somewhat arbitrary, an LR+ </li></ul><ul><li>value of 10 or greater is often perceived as in indication </li></ul><ul><li>of a test of high diagnostic value </li></ul>INTERPRETATION OF LIKELIHOOD RATIOS
74. 81. INTERPRETATION OF LIKELIHOOD RATIOS <ul><li>An LR- with a value of one indicates a test with no value </li></ul><ul><li>in sorting out persons with and without the disease of </li></ul><ul><li>interest as the probability of a negative test result is </li></ul><ul><li>equally likely among persons affected and unaffected. </li></ul><ul><li>The smaller the value of the LR-, the stronger the </li></ul><ul><li>association between having a negative test result and </li></ul><ul><li>not having the disease of interest. </li></ul>
75. 82. INTERPRETATION OF LIKELIHOOD RATIOS <ul><li>The smaller the size of the LR-, the better the </li></ul><ul><li>diagnostic value of the test. On somewhat arbitrary </li></ul><ul><li>grounds, an LR- value of 0.1 or less is often perceived </li></ul><ul><li>as an indication of a test with high diagnostic value. </li></ul>
76. 83. TECHNIQUES FOR USING LIKELIHOOD RATIOS <ul><li>Mathematical approach </li></ul><ul><li>Using a likelihood ratio nomogram </li></ul><ul><li>Simple “Rule of Thumb” for determining effect of likelihood ratios on disease probability </li></ul>
77. 84. Mathematical Approach <ul><li>Convert Pretest Probability (Prevalence) to Pretest odds </li></ul><ul><ul><li>Pretest odds = Prevalence / (1 – Prevalence) </li></ul></ul><ul><li>Multiply Pretest odds by Likelihood ratio to obtain Posttest odds </li></ul><ul><ul><li>Pretest odds X Likelihood ratio = Posttest odds </li></ul></ul><ul><li>Convert Posttest odds to Posttest probability (predictive value) </li></ul><ul><ul><li>Posttest probability = Posttest odds / (1 + Posttest odds) </li></ul></ul>
78. 85. USING A LIKELIHOOD RATIO NOMOGRAM <ul><li>Place a straight edge at the correct prevalence and likelihood ratio values and read off the posttest probability where the straight edge crosses the line </li></ul>
79. 86. LIKELIHOOD RATIO NOMOGRAM
80. 88. SIMPLE “RULE OF THUMB” <ul><li>Approximate Change in </li></ul><ul><li>Likelihood ratio Disease Probability (%) </li></ul><ul><li>10 +45 </li></ul><ul><li>9 +40 </li></ul><ul><li>8 </li></ul><ul><li>7 </li></ul><ul><li>6 +35 </li></ul><ul><li>5 +30 </li></ul><ul><li>4 +25 </li></ul><ul><li>3 +20 </li></ul><ul><li>2 +15 </li></ul><ul><li>1 No Change </li></ul><ul><li>0.5 - 15 </li></ul><ul><li>0.4 - 20 </li></ul><ul><li>0.3 - 25 </li></ul><ul><li>0.2 - 30 </li></ul><ul><li>0.1 - 45 </li></ul>
81. 89. SIMPLE “RULE OF THUMB” <ul><li>Mnemonic </li></ul><ul><ul><li>Likelihood ratio of 2, 5, 10 increases the probability of disease approximately 15%, 30% and 45% respectively, and the inverse of these likelihood ratios of 0.5, 0.2, and 0.1 decrease the probability of disease similarly 15%, 30%, and 45% </li></ul></ul>
82. 90. LIKELIHOOD RATIOS <ul><li>Likelihood ratios must be used with odds, not probability </li></ul><ul><li>The main advantage of likelihood ratios is that they make it possible to go beyond the simple and clumsy classification of a test result as either abnormal or normal, as is usually done when describing the accuracy of a diagnostic test only I terms of sensitivity and specificity at a single cutoff point. </li></ul>
83. 91. LIKELIHOOD RATIOS <ul><li>Disease is more likely in the presence of an extremely abnormal test result than it is for a marginal one </li></ul><ul><li>With likelihood ratios, it is possible to summarize information contained in a test result at different levels </li></ul><ul><li>In computing likelihood ratios across range of test results, a limitation of sensitivity and specificity is overcome. </li></ul>
84. 92. LIKELIHOOD RATIOS <ul><li>Can accommodate the common and reasonable, clinical practice of putting more weight on extremely high (or low) test results than on borderline ones when estimating the probability (or odds) that a particular disease is present. </li></ul>
85. 93. Distribution of Values for Serum Thyroxine in Hypothyroid and Normal Patients, With Calculation of Likelihood Ratios <ul><li>Patients with Test Result </li></ul><ul><li>Total Serum </li></ul><ul><li>Thyroxine Hypothyroid Normal Likelihood Ratio </li></ul><ul><li>(Ug/dL) number (percent) number (percent) </li></ul><ul><li><1.1 2(7.4) </li></ul><ul><li>1.1 – 2.0 3(11.1) Ruled in </li></ul><ul><li>2.1 – 3.0 1(3.7) </li></ul><ul><li>3.1 – 4.0 8(29.6) </li></ul><ul><li>4.1 – 5.0 4(14.8) 1(1.1) 13.8 </li></ul><ul><li>5.1 – 6.0 4(14.8) 6(6.5) 2.3 </li></ul><ul><li>6.1 – 7.0 3(11.1) 11(11.8) .9 </li></ul><ul><li>7.1 – 8.0 2(7.4) 19(20.4) .4 </li></ul><ul><li>8.1 – 9.0 17(18.3) </li></ul><ul><li>9.1 – 10 20(21.5) </li></ul><ul><li>10.1 – 11 11(11.8) Ruled out </li></ul><ul><li>11.1 – 12 4(4.3) </li></ul><ul><li>> 12 4(4.3) </li></ul><ul><li>Total 27(100) 93(100) </li></ul>
86. 94. DISEASE Clinical question: How accurate are tests to diagnose disease ? <ul><li>Problems: </li></ul><ul><li>Lack of information on negative tests </li></ul><ul><li>Lack of information on test results in the nondiseased </li></ul><ul><li>Lack of objective standards for disease </li></ul><ul><li>Consequences of imperfect standards </li></ul><ul><li>If a new test is compared with an old (but inaccurate) standard test, the new test may seem worse even when it is actually better </li></ul>
87. 95. DISEASE Clinical question: How accurate are tests to diagnose disease? <ul><li>Reliability and validity </li></ul><ul><li>Measurement error </li></ul><ul><li>Instrument The means of making the measurement </li></ul><ul><li>Observer The person making the measurement </li></ul><ul><li>Biologic variation </li></ul><ul><li>Within individuals Changes in people with time and situation </li></ul><ul><li>Among individuals Biologic differences from person to person </li></ul>
88. 96. DISEASE Clinical question: How accurate are tests to diagnose disease?
89. 97. EARLY DIAGNOSIS <ul><li>Strategies </li></ul><ul><ul><li>Screening test (uni- or multi-phasic) </li></ul></ul><ul><ul><li>Periodic health examination </li></ul></ul><ul><ul><li>Case finding </li></ul></ul><ul><li>Objectives </li></ul><ul><ul><li>Early detection of asymptomatic disease </li></ul></ul><ul><ul><li>Identification of predictors or risk factors of disease </li></ul></ul>
90. 98. EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) <ul><li>Biologic onset </li></ul><ul><ul><li>initial interaction between man, causal </li></ul></ul><ul><ul><li>factors, and the rest of the environment </li></ul></ul><ul><ul><li>cannot detect the presence of disease </li></ul></ul><ul><li>Early diagnosis possible </li></ul><ul><ul><li>mechanisms of disease produce structural or functional changes </li></ul></ul><ul><ul><li>individual remains free of any symptoms </li></ul></ul>
91. 99. EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) <ul><li>Usual clinical diagnosis </li></ul><ul><ul><li>disease progresses to the point where </li></ul></ul><ul><ul><li>symptoms appear and affected individual becomes ill </li></ul></ul><ul><li>Outcome </li></ul><ul><ul><li>recovery, permanent disability or </li></ul></ul><ul><ul><li>death </li></ul></ul>
92. 100. EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) T I M E EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME Recovery Disability Death D X
93. 101. EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE 1 2 3 CP CP CP EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME Recovery Disability Death D X
94. 102. EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE <ul><li>Position 1 </li></ul><ul><li>The screening test and case finding would be too late to be of help in early detection of disease </li></ul><ul><li>Position 2 </li></ul><ul><li>The test will have a promise of improving the outcomes of those who have the target disorder </li></ul><ul><li>Position 3 </li></ul><ul><li>Early detection of the disease is a waste of time </li></ul>
95. 103. EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE <ul><li>How do we tell a disease has a </li></ul><ul><li>critical point at position 2 and its </li></ul><ul><li>detection is worth our critical </li></ul><ul><li>effort? </li></ul>
96. 104. EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE Data modified from S. Shapiro. Evidence of screening for breast cancer from a randomized trial (Suppl.) 39:2772, 1977 Breast cancers diagnosed early in the Health Insurance Plan Study Age at diagnosis Percentage with positive axillary nodes 40-49 50-59 60+ Total Mode of early diagnosis Only by mammography 6 (19%) 27 (42%) 11 (31%) 44 (33%) 16% Only by clinical exam 19 (62%) 26 (40%) 14 (38%) 59 (45%) 19% Detected by both modes 6 (19%) 12 (18%) 11 (31%) 29 (22%) 41% 31 (100%) 65 (100%) 36 (100%) 132 (100%)
97. 105. EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE Some results of the H.I.P. randomized trial of early diagnosis in breast cancer Data modified from S. Shapiro. Evidence of screening for breast cancer from a randomized trial, Cancer(Suppl.) 39:2772, 1977 Deaths per 10,000 women per year From breast cancer From all other causes From cardiovascular disease 40-49 50-59 60-69 Control women 2.4 5.0 5.0 54 25 Experimental women 2.5 2.3 3.4 54 24
98. 106. HOW TO DECIDE WHEN TO SEEK AN EARLY DIAGNOSIS <ul><li>Does early diagnosis really lead to improved clinical outcomes ( in terms of survival, function, and quality of life)? </li></ul><ul><li>Can you manage the additional clinical time required to confirm the diagnosis and provide long-term care for those screen positive? </li></ul><ul><li>Will the patients in whom an early diagnosis is achieved comply with your subsequent recommendations and treatment regimen </li></ul>
99. 107. HOW TO DECIDE WHEN TO SEEK AN EARLY DIAGNOSIS <ul><li>Has the effectiveness of individual components of a periodic health examination or multiphasic screening program been demonstrated prior to their combination? </li></ul><ul><li>Does the burden of disability from the target disease warrant action? </li></ul><ul><li>Are the cost, accuracy, and acceptability of the screening test adequate for your purpose? </li></ul>
100. 108. Does early diagnosis really lead to improved clinical outcomes (in terms of survival, function, and quality of life)? <ul><li>Claims for therapeutic benefit must withstand close scrutiny and experimental evidence from randomized trials is a prerequisite. </li></ul><ul><li>Long-term beneficial effects of therapy outweigh the long-term detrimental effects of the treatment regimen and labeling of patients as diseased. </li></ul>
101. 109. Can you manage the additional clinical time required to confirm the diagnosis and provide long-term care for those screen positive? <ul><ul><li>Increased demands on your time start with early diagnosis and you need to be sure that you have enough of it. </li></ul></ul><ul><ul><li>Large numbers of labeled but untreated hypertensive attest to the size of this problem </li></ul></ul>
102. 110. Will the patients in whom an early diagnosis is achieved comply with your subsequent recommendations and treatment regimens <ul><li>If patients will not take their medicine, all the screening and diagnosis made are nullified. </li></ul><ul><li>Labeled patient </li></ul>
103. 111. Have the effectiveness of individual components of a periodic health examination or multiphasic screening program been demonstrated prior to their combination? <ul><li>The appropriateness of a mix of tests must consider whether differences in the distributions of two diseases render the combination of their respective screening tests nonsensical. </li></ul><ul><li>It was this consideration that led the Canadian Task Force on the Periodic Health Examination to propose quite different “health protection packages” for patients of different age, sex, and social status. </li></ul>
104. 112. Does the burden of disability from the target disease warrant action? <ul><li>The disease you are searching for should be either so common or so awful as to warrant all the work and expense of detecting it in its presymptomatic state </li></ul>
105. 113. Types of epidemiological study <ul><li>Type of study Alternative name Unit of study </li></ul><ul><li>Observational studies </li></ul><ul><li>Descriptive studies </li></ul><ul><li>Analytical studies </li></ul><ul><li> Ecological Correlational Population </li></ul><ul><li>Cross-sectional Prevalence Individuals </li></ul><ul><li>Case-control Case-reference Individuals </li></ul><ul><li>Cohort Follow-up Individuals </li></ul><ul><li>Experimental studies Interventional studies </li></ul><ul><li>Randomized controlled trials Clinical trials Patients </li></ul><ul><li>Field trials Healthy people </li></ul><ul><li>Community trials Community intervention Communities </li></ul><ul><li>studies </li></ul>
106. 114. Types of epidemiological study (Descriptive studies) <ul><li>Case reports </li></ul><ul><li>- detailed presentations of a single case or a handful of cases </li></ul><ul><li> - means of describing rare clinical events </li></ul><ul><li>- describe unusual manifestations of disease </li></ul><ul><li>- elucidate the mechanisms of disease and treatment </li></ul><ul><li>- place issues before medical community and often trigger </li></ul><ul><li>more decisive studies </li></ul><ul><li>- susceptible to bias </li></ul>
107. 115. Types of epidemiological study (Descriptive studies) <ul><li>Case-series </li></ul><ul><li>- a simple descriptive account of interesting characteristics observed in a group of patients </li></ul><ul><li>- study larger group of patients (e.g. 10 or more) with particular disease </li></ul><ul><li>- describe the clinical manifestations of disease and treatments in a group of patients assembled at one point in time </li></ul><ul><li>- absence of a comparison group, not conclusive </li></ul><ul><li>- hypothesis-generating </li></ul><ul><li>- selection bias </li></ul>
108. 116. Types of epidemiological study (Observational studies) <ul><li>Ecological studies </li></ul><ul><li>- aggregate risk studies </li></ul><ul><li>- units of analysis are populations or groups of people rather </li></ul><ul><li>than individuals </li></ul><ul><li>- rely on data collected for other purposes; data on different exposures and on socioeconomic factors may not be available </li></ul><ul><li>- ecological fallacy (bias) </li></ul><ul><li>- useful in raising hypothesis </li></ul>
109. 117. Types of epidemiological study (Observational studies) Study subjects With outcome Without outcome Population at risk Defined population Onset of study TIME No direction of inquiry Cross-sectional study (Prevalence study)
110. 118. Types of epidemiological study (Observational Studies) <ul><li>Cross-sectional studies (Prevalence studies) </li></ul><ul><li>- measure the prevalence of disease </li></ul><ul><li>- measurements of exposure and effect are made at </li></ul><ul><li>the same time </li></ul><ul><li>- useful for investigating exposures that are fixed </li></ul><ul><li>characteristics of individuals, such as ethnicity, </li></ul><ul><li>socio-economic status and blood group, or chronic </li></ul><ul><li>diseases or stable conditions </li></ul>
111. 119. Types of epidemiological study (Observational studies) <ul><li>Cross-sectional studies (Prevalence studies) </li></ul><ul><li>- In sudden outbreaks of disease it is the most convenient first step in an investigation into the cause </li></ul><ul><li>- Rare disease, conditions of short duration or diseases with high case fatality are often not detected </li></ul>
112. 120. Types of epidemiological study (Observational studies) <ul><li>Cross-sectional studies (Prevalence studies) </li></ul><ul><li>- short-term and therefore less costly </li></ul><ul><li>- provide no direct estimate of risk </li></ul><ul><li>- prone to bias from selective survival </li></ul><ul><li>- estimates of prevalence may be biased by the exclusion of cases in which death or recovery are rapid </li></ul>
113. 121. Types of epidemiological study (Observational studies) CASES (people with disease) CONTROLS (people without disease) Exposed Not exposed Exposed Not exposed Population direction of inquiry T I M E Design of a case-control study
114. 122. Types of epidemiological study (Observational studies) <ul><li>Case-control studies </li></ul><ul><li>- longitudinal studies (looking backward from the disease to a possible cause) </li></ul><ul><li>- use new (incident) cases </li></ul><ul><li>- used to investigate cause (etiology) of disease, esp. rare diseases </li></ul><ul><li>- used odds ratio </li></ul>
115. 123. Types of epidemiological study (Observational studies) <ul><li>Case-control studies </li></ul><ul><li>- relatively efficient, requiring smaller sample than cohort study </li></ul><ul><li>- completed faster and more economical </li></ul><ul><li>- earliest practical observational strategy for determining an association </li></ul><ul><li>- antecedent-consequence uncertainty </li></ul>
116. 124. Table arrangement and formula for Odds ratio (OR) <ul><li>Disease No disease Total </li></ul><ul><li>Risk factor present A B A+B </li></ul><ul><li>Risk factor absent C D C+D </li></ul><ul><li>Total A+C B+D </li></ul><ul><li>[A / (A+C)] / [C / (A+C)] A/C AD </li></ul><ul><li>OR = ------------------------------- = ------- = ------- </li></ul><ul><li>[B / (B+D)] / [D / (B+D)] B/D BC </li></ul>
117. 125. Types of epidemiological study (Observational study) <ul><li>Interpretation of Odds ratio </li></ul><ul><li>Value of OR less than 1 indicates a negative association (i.e., protective effect) between the risk factor and the disease </li></ul><ul><li>For rare disease (e.g., most chronic diseases with disease prevalence of less than 10%), OR approximates RR </li></ul>
118. 126. Example of case-control study <ul><li>Association between recent meat consumption and enteritis necroticans in Papua New Guinea </li></ul><ul><li>Exposure </li></ul><ul><li>(recent meat ingestion) </li></ul><ul><li>Yes No Total </li></ul><ul><li>Disease Yes 50 11 61 </li></ul><ul><li>(enteritis necroticans) No 16 41 57 </li></ul><ul><li>Total 66 52 118 </li></ul>
119. 127. Example of case-control study <ul><li>[A / (A+C)] / [C / (A+C)] A / C AD </li></ul><ul><li>OR = -------------------------------- = -------- = ----- </li></ul><ul><li>[B / (B+D)] / [D / (B+D)] B / D BD </li></ul><ul><li>50 X 41 </li></ul><ul><li>OR = ------------- = 11.6 </li></ul><ul><li>11 X 16 </li></ul><ul><li>The cases were 11.6 times more likely than the controls to have recently ingested meat </li></ul>
120. 128. Types of epidemiological study (Observational studies) Population People without the disease Exposed Not exposed disease no disease disease no disease direction of inquiry T I M E Design of a cohort study
121. 129. Past Present Future Cohort Follow-up assembled Historical cohort Cohort Follow-up assembled Concurrent cohort
122. 130. Types of epidemiological study (Observational studies) <ul><li>Cohort studies </li></ul><ul><li>- longitudinal studies (forward) </li></ul><ul><li>- provide the best information about the causation of disease </li></ul><ul><li>- most direct measurement of the risk of developing disease </li></ul><ul><li>- provide the possibility of estimating the attributable risks </li></ul><ul><li>- use relative risk </li></ul>
123. 131. Types of epidemiological study (Observational studies) <ul><li>Cohort studies </li></ul><ul><li>- most closely resemble experimental studies </li></ul><ul><li>- Long-term, not always feasible </li></ul><ul><li>- Sample size required for the study extremely large </li></ul><ul><li>- Attrition is most serious problem </li></ul>
124. 132. Table arrangement and formula for relative risk (RR) Disease No Disease Total Risk factor present A B A + B Risk factor absent C D C + D Total A + C B + D A / (A + B) RR = ----------------- C / (C + D)
125. 133. Types of epidemiological study (Observational studies) <ul><li>Interpretation of relative risk (RR) </li></ul><ul><li>The disease (or other health related outcome) is RR times more likely to occur among those exposed than among those with no exposure </li></ul><ul><li>The larger the value of RR , the stronger the association between the disease in question and exposure to the risk factor </li></ul>
126. 134. Types of epidemiological study (Observational studies) <ul><li>Interpretation of relative risk (RR) </li></ul><ul><li>Value of RR close to 1 indicates that the disease and exposure to the risk factor are unrelated </li></ul><ul><li>Value of RR less than 1 indicates a negative association between the risk factor and the disease (i.e., protective rather than detrimental) </li></ul>
127. 135. Example of cohort study <ul><li>Problem: </li></ul><ul><li>A county school system provides lunch to 10,000 </li></ul><ul><li>school children. During the first week of school, 2,500 </li></ul><ul><li>of these children ate chicken salad later shown to be </li></ul><ul><li>contaminated with salmonella. The entire population </li></ul><ul><li>of 10,000 students was subsequently followed for one </li></ul><ul><li>month to determine whether exposure to salmonella </li></ul><ul><li>increased the risk of diarrhea. </li></ul>
128. 136. Example of cohort study <ul><li> Diarrhea No Diarrhea </li></ul><ul><li>Exposure (D+) (D-) Totals </li></ul><ul><li>E+ 30 2,470 2,500 </li></ul><ul><li>E- 60 7,440 7,500 </li></ul><ul><li>Totals 90 9,910 10,000 </li></ul>A / (A+B) 30 / 2,500 RR = --------------- = ----------------- = 1.5 C / (C+D) 60 / 7,500 1.5 times greater than in children with no such exposure
129. 137. Advantages and disadvantages of different observational study designs Probability of: selection bias NA medium high low recall bias NA high high low loss to follow-up NA NA low high confounding high medium medium low Time required low medium medium high Cost low medium medium high Ecological Cross- Case- Cohort sectional control
130. 138. Applications of different observational study designs Investigation of rare disease ++++ - +++++ - Investigation of rare cause ++ - - +++++ Testing multiple effect of + ++ - +++++ cause Study of multiple exposure ++ ++ ++++ +++ and determinants Measurements of time ++ - + +++++ relationship Direct measurement of - - + +++++ incidence Investigation of long - - +++ - latent periods Ecological Cross- Case- Cohort sectional control
131. 139. Types of epidemiological study (Experimental studies) Non-participants Do not meet Selection criteria Potential participants Participants Non-participants Control Treatment Study population Randomization Invitation to participate Selection by defined criteria Design of a randomized clinical trial
132. 140. Types of epidemiological study (Experimental studies) <ul><li>Randomized controlled trials (RCTs) </li></ul><ul><li>Gold standard or reference in medicine </li></ul><ul><li>Provide the greatest justification for concluding causality </li></ul><ul><li>Subject to the least number of problems or biases </li></ul><ul><li>Best study design to establish the efficacy of a treatment or a procedure </li></ul>
133. 141. Types of epidemiological study (Experimental studies) <ul><li>Randomized controlled trials (RCTs) </li></ul><ul><li>- Expensive and time-consuming </li></ul><ul><li>- Difficult to obtain approval to perform properly designed clinical trials </li></ul>
134. 142. Relative ability of different types of study to “prove” causation Type of study Ability to “prove” causation Randomized controlled trials Strong Cohort studies Moderate Case-control studies Moderate Cross-sectional studies Weak Ecological studies Weak
135. 143. Bias in Clinical Observation <ul><li>Selection bias occurs when comparisons are </li></ul><ul><li>made between groups of patients that differ in </li></ul><ul><li>determinants of outcome other than the one under </li></ul><ul><li>study </li></ul><ul><li>Measurement bias occurs when the methods of </li></ul><ul><li>measurement are dissimilar among groups of </li></ul><ul><li>patients </li></ul><ul><li>Confounding bias occurs when two factors are </li></ul><ul><li>associated (“travel together”) and the effect of one </li></ul><ul><li>is confused with or distorted by the effect of the </li></ul><ul><li>other </li></ul>
136. 144. Methods of Controlling Selection Bias <ul><li>Phase of Study </li></ul><ul><li>Method Description Design Analysis </li></ul><ul><li>Randomization Assign patients to groups in a way that + </li></ul><ul><li>gives each patient equal chance of </li></ul><ul><li>falling into one or the other group </li></ul><ul><li>Restriction Limit the range of characteristics of + </li></ul><ul><li>of patients in the study </li></ul><ul><li>Matching For each patient in one group select one + </li></ul><ul><li>or more patients with the same </li></ul><ul><li>characteristics (except for the one </li></ul><ul><li>under study) for a comparison group </li></ul><ul><li>Stratification Compare rates within subgroups (strata) + </li></ul><ul><li>with otherwise similar probability of the </li></ul><ul><li>outcome </li></ul>
137. 145. Methods for Controlling Selection Bias <ul><li>Phase of Study </li></ul><ul><li>Method Description Design Analysis </li></ul><ul><li>Adjustment </li></ul><ul><li>Simple Mathematically adjust crude rates for one + </li></ul><ul><li>or few characteristics so that equal weight </li></ul><ul><li>is given to strata of similar risk </li></ul><ul><li>Multiple Adjust for difference in large number of factors + </li></ul><ul><li>related to outcome, using mathematical </li></ul><ul><li>modelling techniques </li></ul><ul><li>Best case/ Describe how different the results could be + </li></ul><ul><li>Worse case under the most extreme or simply very unlikely) </li></ul><ul><li>conditions of selection bias </li></ul>
138. 146. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Webster’s definition: “something that brings about an effect or a result” </li></ul><ul><li>“ A factor is a cause of an event if its operation increases the frequency of an event” </li></ul><ul><li>In medicine : “etiology” “pathogenesis” “mechanisms” or “risk factors” </li></ul><ul><li>Importance: prevention, diagnosis and treatment of disease </li></ul>
139. 147. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Concepts of Cause </li></ul><ul><li>Single causation (Koch’s postulates) </li></ul><ul><li>a particular disease has one cause and a particular cause results in one disease </li></ul><ul><li>The organism must be present in every case of the disease </li></ul><ul><li>The organism must be isolated and grown in pure culture </li></ul><ul><li>The organism must cause a specific disease when inoculated into an animals and </li></ul><ul><li>The organism must then be recovered from the animal and identified </li></ul>
140. 148. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Multiple causation (Web of causation) </li></ul><ul><li>Effects never depend on single isolated causes, but rather develop as the result of chains of causation in which each link itself is the result of “a complex genealogy of antecedents.” </li></ul><ul><li>Many factors act together to cause disease </li></ul>
141. 149. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Concept of Cause </li></ul><ul><li>A cause must precede a disease </li></ul><ul><li>A cause is termed sufficient when it inevitably produces or initiates a disease </li></ul><ul><li>A cause is termed necessary if a disease cannot develop in its absence </li></ul>
142. 150. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? INCREASED SUSCEPTIBILITY INGESTION OF CHOLERA VIBRIO CHOLERA Causes of cholera Exposure to contaminated water Effect of cholera toxins on bowel wall cells Genetic factors Malnutrition Crowded housing Poverty Risk factors for cholera Mechanisms for cholera
143. 151. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>A sufficient cause is not usually a single factor, but often comprises several components </li></ul><ul><li>It is not necessary to identify all the components of a sufficient cause before effective prevention can take place </li></ul><ul><li>Each sufficient cause has a necessary cause as a component </li></ul><ul><li>A causal factor on its own is often neither necessary nor sufficient </li></ul>
144. 152. SUFFICIENT CAUSES U A B U A E U B E I II III
145. 153. Causation <ul><li>Causal relationship in the physical sciences are often simple, as in Boyle’s law relating pressure and volume of a gas, or the effect of heat on a metal bar. The causal agent is sufficient, the time relationship is short, and replication is easy. </li></ul><ul><li>In the Boyle’s law situation, a change in pressure was both necessary and sufficient for a change in volume, given that the other circumstances were fixed. </li></ul>
146. 154. Causation <ul><li>In the metal bar example, heat was sufficient but not a necessary cause; there are other ways of lengthening a metal bar </li></ul><ul><li>Causal relationship in human health and disease are rarely simple </li></ul>
147. 155. Causation <ul><li>In human health and disease not all causal agents are sufficient. </li></ul><ul><li>In the disease tuberculosis, infection by the tubercle bacillus does not invariably lead to clinical tuberculosis. Only a small proportion of those who are infected by the bacillus develop clinical tuberculosis </li></ul>
148. 156. Causation <ul><li>Most situations in health and disease do not fulfill the criteria either necessary or for sufficient causation. </li></ul><ul><li>An healthy man is admitted to hospital with multiple fractures, having been hit by a bus just outside the hospital </li></ul>
149. 157. Causation <ul><li>We can conclude that there was a causal relationship between being hit by the bus and having multiple fractures </li></ul><ul><li>But the relationship implies neither that the cause is sufficient nor that it is necessary. </li></ul><ul><li>Not all people hit by buses have multiple fractures. Not all patients with multiple fractures have been hit by buses. </li></ul>
150. 158. Causation <ul><li>Where the time relation is not clear, and the concepts of necessary and sufficient cause do not hold, we need a quantitative assessment of the relationship, based on observations not on one individual but on a number of individuals. Hence, the definition of causation is quantitative </li></ul>
151. 159. Causation <ul><li>A direct test of the quantitative definition of causation is by randomized trial approach </li></ul>
152. 160. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Concept of cause </li></ul><ul><li>Proximity of cause to effect </li></ul><ul><li>Disease is also determined by less specific, more remote causes or risk factors, such as people’s behavior or characteristics of their environment. </li></ul><ul><li>These factors may be even more important causes of disease than are pathogenetic mechanisms </li></ul><ul><li>If the pathogenetic mechanism is not clear, knowledge of </li></ul><ul><li>risk factors may still lead to very effective treatments and </li></ul><ul><li>prevention </li></ul>
153. 161. SUSCEPTIBLE HOST INFECTION TUBERCULOSIS Exposure to Mycobacterium Tissue Invasion and Reaction Crowding Malnutrition Vaccination Genetic Risk Factors for Mechanisms of Tuberculosis Pathogenesis Tuberculosis Distant from Outcome Proximal to Outcome Causes of tuberculosis
154. 162. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Concept of cause </li></ul><ul><li>Interplay of multiple causes </li></ul><ul><li>Synergism – the joint effect is greater than the sum of the effects of the individual causes </li></ul><ul><li>Antagonism – the joint effect is lesser </li></ul><ul><li>Effect Modification – a special type of interaction </li></ul><ul><li>A substantial impact on a patient’s health by changing </li></ul><ul><li>only one or a small number of the causes </li></ul>
155. 163. Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Risk refers to the probability of some untoward event </li></ul><ul><li>Risk indicates the likelihood that people who are exposed to certain factors (risk factors) will subsequently develop a particular disease </li></ul><ul><li>Risk factor refers to condition, physical characteristic, or behavior that increases the probability (i.e., risk) that a currently healthy individual will develop a particular disease. </li></ul>
156. 164. Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanism of disease ? <ul><li>Exposure to risk factor can occur at a single point in time or over a period of time </li></ul><ul><li>ever exposed </li></ul><ul><li> current dose </li></ul><ul><li>largest dose taken </li></ul><ul><li>total cumulative dose </li></ul><ul><li>years of exposure </li></ul><ul><li>years since first contact </li></ul>
157. 165. Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Recognizing risk </li></ul><ul><li>Large risks associated with effects that occur rapidly after exposure are easy for anyone to recognize </li></ul><ul><li>Most morbidity and mortality are caused by chronic diseases. The relationship between exposure and disease are far less obvious – latency period </li></ul>
158. 166. Comparing disease occurrence among exposed and unexposed <ul><li>Absolute comparison </li></ul><ul><ul><li>Risk difference, also called attributable risk (exposed), excess risk or absolute risk </li></ul></ul><ul><ul><li>Attibutable fraction (exposed) or etiological fraction (exposed) </li></ul></ul><ul><ul><li>Population attributable risk or attributable fraction (population) </li></ul></ul><ul><li>Relative comparison </li></ul><ul><ul><li>Risk ratio </li></ul></ul><ul><ul><li>Standardized mortality ratio </li></ul></ul>
159. 167. Relationship between cigarette smoking and incidence rate of stroke in a cohort of 118,539 women Never smoked 70 395,594 17.7 Ex-smoker 65 232,712 27.9 Smoker 139 280,141 49.6 Total 274 908,447 30.2 Smoking Person-y ears Stroke incidence rate category No. of cases of observation (per 100,000 of stroke (over 8 years) person-years)
160. 168. Comparing disease occurrence among exposed and unexposed <ul><li>Risk difference </li></ul><ul><li>is the difference in rates of occurrence between exposed and unexposed groups </li></ul><ul><li>useful measure of the extent of the public health problem caused by the exposure </li></ul><ul><li>Example: </li></ul><ul><li>49.6 – 17.7 = 31.9 per 100,000 person-years </li></ul>
161. 169. Comparing disease occurrence among exposed and unexposed <ul><li>Attributable fraction (exposed) </li></ul><ul><li>is the proportion of the disease in the specific population that would be eliminated in the absence of exposure </li></ul><ul><li>determined by dividing the risk difference by the rate of occurrence among the exposed population </li></ul><ul><li>Example: </li></ul><ul><li>[(49.6 – 17.7) / 49.6] x 100 = 64% </li></ul><ul><li>Interpretation: One would expect to achieve a 64% reduction in the risk of stroke among the women smokers if smoking were stopped, on the assumption that smoking is both causal and preventable </li></ul>
162. 170. Comparing disease occurrence among exposed and unexposed <ul><li>Population attributable risk [attributable fraction (population)] </li></ul><ul><li>is a measure of the excess rate of disease in a total study population which is attributable to an exposure </li></ul><ul><li>useful for determining the relative importance of exposures for the entire population and is the proportion by which the incidence rate of the outcome in the entire population would be reduced if exposure were eliminated. </li></ul>30.2 – 17.7 = ------------------ = 0.414 o r 41.4% 30.2
163. 171. Comparing disease occurrence among exposed and unexposed <ul><li>Risk ratio or relative risk </li></ul><ul><li>the ratio of the risk of occurrence of a disease among exposed people to that among the unexposed </li></ul><ul><li>better indicator of the strength of an association than the risk difference </li></ul><ul><li>used in assessing the likelihood that an association represents a causal relationship </li></ul><ul><li>Example: </li></ul><ul><li>RR = 49.6 / 17.7 = 2.8 </li></ul>
164. 172. Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Uses of risk factor </li></ul><ul><li>predict the occurrence of disease </li></ul><ul><li>marker of disease outcome </li></ul><ul><li>improve the positive predictive value of a diagnostic test </li></ul><ul><li>prevent disease </li></ul>
165. 173. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Establishing cause </li></ul><ul><li>In clinical medicine, it is not possible to prove causal relationship beyond any doubt. It is only possible to increase one’s conviction of a cause and effect relationship, by means of empiric evidence, cause is established. </li></ul>
166. 174. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Establishing cause </li></ul><ul><li>Factors that are considered causes at one time are sometimes found to be indirectly related to disease later, when more evidences are available </li></ul>
167. 175. Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? <ul><li>Establishing cause </li></ul><ul><li>Two factors – the suspected cause and the effect – obviously must appear to be associated if they are to be considered as cause and effect </li></ul><ul><li>However, not all associations are causal </li></ul><ul><li>Two factors may be associated but not causal </li></ul><ul><li>due to the presence of selection and measurement biases, chance and confounder </li></ul>
168. 176. Could it be due to selection or measurement bias Could it be due to confounding? Could it be a result of chance? Could it be causal? Apply guidelines and make judgment ASSESSING THE RELATIONSHIP BETWEEN A POSSIBLE CAUSE AND OUTCOME No No Probably not
169. 177. GUIDELINES FOR CAUSATION <ul><li>Temporal Does the cause precede the effect ? </li></ul><ul><li>relationship (essential) </li></ul><ul><li>Plausibility Is the association consistent with other </li></ul><ul><li>knowledge ? </li></ul><ul><li> (mechanism of action; evidence from </li></ul><ul><li>experimental animals) </li></ul><ul><li>Consistency Have similar results been shown in other </li></ul><ul><li>studies ? </li></ul><ul><li>Strength What is the strength of the association </li></ul><ul><li>between the cause and the effect ? </li></ul><ul><li>(relative risk) </li></ul>
170. 178. GUIDELINES FOR CAUSATION <ul><li>Dose-response Is increased exposure to the </li></ul><ul><li>relationship possible cause associated with </li></ul><ul><li>increased effect ? </li></ul><ul><li>Reversibility Does the removal of a possible cause lead </li></ul><ul><li>to reduction of disease risk ? </li></ul><ul><li>Study design Is the evidence based on a strong study </li></ul><ul><li>design ? </li></ul><ul><li>Judging the How many lines of evidence lead </li></ul><ul><li>evidence to conclusions? </li></ul>
171. 180. Treatment Clinical question: How does treatment change the course of disease? DECIDING ON THE BEST THERAPY
172. 181. <ul><li>IS THE ULTIMATE OBJECTIVE TO ACHIEVE CURE, PALLIATION, SYMPTOMATIC RELIEF, OR WHAT? </li></ul><ul><li>DOES THE PATIENT REQUIRE ANY TREATMENT AT ALL? </li></ul><ul><li>WHAT SORTS OF EVIDENCE, FROM WHAT SOURCES, SHOULD DETERMINE THE CHOICE OF THE SPECIFIC TREATMENT TO BE USED TO REACH THIS GOAL </li></ul><ul><li>HOW WILL YOU KNOW WHEN TO STOP TREATMENT, CHANGE ITS INTENSITY, OR SWITCH TO SOME OTHER TREATMENT? </li></ul>THREE PRINCIPAL DECISIONS THAT DETERMINE THE RATIONAL TREATMENT OF ANY PATIENT
173. 182. Example <ul><li>A PATIENT WITH SYMPTOMLESS BUT </li></ul><ul><li>MODERATELY SEVERE ESSENTIAL </li></ul><ul><li>HYPERTENSION (FIFTH-PHASE </li></ul><ul><li>DIASTOLIC BLOOD PRESSURE 110 mm </li></ul><ul><li>Hg). </li></ul>
174. 183. Example <ul><li>ULTIMATE OBJECTIVE OF TREATMENT </li></ul><ul><ul><li>To prevent (further) target organ damage to the </li></ul></ul><ul><ul><li>brain, eye, heart, kidney, and large vessels that </li></ul></ul><ul><ul><li>would cause disability or untimely death. </li></ul></ul><ul><li>CHOICE OF SPECIFIC TREATMENT </li></ul><ul><ul><li>On the basis of randomized clinical trials of active </li></ul></ul><ul><ul><li>agents versus placebo, antihypertensive drugs </li></ul></ul><ul><li>TREATMENT TARGET </li></ul><ul><ul><li>A fifth-phase diastolic blood pressure of less than 90 </li></ul></ul><ul><ul><li>mm Hg, or as close to that as tolerable in the face of </li></ul></ul><ul><ul><li>drug side effects. </li></ul></ul>
175. 184. SIX OBJECTIVES OF TREATMENT <ul><li>Cure (e.g. kill the microbe, cut out the tumor, desensitize the phobic patient) </li></ul><ul><li>Prevent a recurrence (e.g. give prophylactic antibiotics following recovery from acute rheumatic fever, or major tranquilizers following discharge for schizophrenia ) </li></ul><ul><li>Limit structural or functional deterioration (e.g. reconstruct, rehabilitate) </li></ul><ul><li>Prevent the later complication (e.g. give diuretics to symptomless hypertensives and aspirin to threatened strokes). </li></ul>
176. 185. SIX OBJECTIVES OF TREATMENT <ul><li>Relieve the current distress (e.g. replace the hormone, provide emotional support or counseling, give painkillers, anti-depressants and anti-inflammatory drugs) </li></ul><ul><li>Deliver reassurance (e.g. “un-label” the misdiagnosed, transmit the truly favorable prognosis) </li></ul><ul><li>Allow to die with comfort and dignity (e.g. cancel further diagnostic testing and focus on the relief of current symptoms and the preservation of self-esteem). </li></ul>
177. 186. THREE ELEMENTS OF A SICKNESS <ul><li>THE DISEASE OR TARGET DISORDER </li></ul><ul><ul><li>THE ANATOMIC, BIOCHEMICAL, PHYSIOLOGIC, OR </li></ul></ul><ul><ul><li>PSYCHOLOGIC DERANGEMENT </li></ul></ul><ul><li>THE ILLNESS </li></ul><ul><ul><li>THE SIGNS, SYMPTOMS, AND BEHAVIORS EXHIBITED </li></ul></ul><ul><ul><li>BY THE PATIENT AS A RESULT OF, AND RESPONDING </li></ul></ul><ul><ul><li>TO, THE TARGET DISORDER </li></ul></ul><ul><li>THE PREDICAMENT </li></ul><ul><ul><li>THE SOCIAL, PSYCHOLOGICAL, AND ECONOMIC </li></ul></ul><ul><ul><li>FASHION IN WHICH THE PATIENT IS SITUATED IN </li></ul></ul><ul><ul><li>THE ENVIRONMENT </li></ul></ul>
178. 187. <ul><li>Need to know exactly what is being treated </li></ul><ul><ul><li>Its prognosis when treated and untreated </li></ul></ul><ul><ul><li>Its risk of relapse and recurrence </li></ul></ul><ul><ul><li>Its permanent disabilities </li></ul></ul><ul><ul><li>Its ultimate outcomes </li></ul></ul><ul><li>Need for the correct and accurate assessment of illness as this is the key to setting treatment objectives (symptomatic relief) </li></ul><ul><li>Need to assess the patient’s predicament in order to identify the limits of one’s treatment options </li></ul>
179. 188. SELECTING THE SPECIFIC TREATMENT <ul><li>The first element of selecting the </li></ul><ul><li>specific treatment is to decide first </li></ul><ul><li>whether any treatment is required. </li></ul>
180. 189. SELECTING SPECIFIC TREATMENT <ul><li>Modern manufacturers have introduced exotic </li></ul><ul><li>machines that can select, punch, drill, bend, fit, </li></ul><ul><li>and weld raw materials into finished goods all by </li></ul><ul><li>themselves, they sharpen their own tools when </li></ul><ul><li>they become dull, replace bits of themselves </li></ul><ul><li>when they wear out, and even sense and correct </li></ul><ul><li>their own mistakes. </li></ul>
181. 190. SELECTING SPECIFIC TREATMENT <ul><li>One problem they have not been able to overcome, </li></ul><ul><li>however, is the almost irresistable temptation they </li></ul><ul><li>present to their human attendants to adjust, reset, </li></ul><ul><li>and otherwise tinker with them, even when they are </li></ul><ul><li>functioning fine. The results are often disastrous. </li></ul><ul><li>In desperation, some plant managers have installed </li></ul><ul><li>prominent notices along their automated assembly lines: </li></ul><ul><li>IF IT AIN’T BROKE, DON’T FIX IT! </li></ul>
182. 191. There are two circumstances in which patients “ain’t broke” and ought not attempt to “fix” them <ul><li>False-positive diagnostic errors that label patients as diseased. </li></ul><ul><li>When either the treatment is worse than the disease or when their illness is trivial, self-limited, or well within the recuperative and reparative powers of the patient’s body and mind </li></ul>
183. 192. DR. CLIFTON MEADOR NICELY SUMMARIZED THESE “NON-DISEASES” <ul><li>MIMICKING SYNDROMES (round-faced fat women with hairy upper lips but normal steroids have non-Cushing’s disease) </li></ul><ul><li>UPPER-LOWER LIMIT SYNDROMES (borderline laboratory values) </li></ul><ul><li>NORMAL VARIATION SYNDROMES (Short children of short parents have non-dwarfism) </li></ul><ul><li>LABORATORY ERROR SYNDROMES </li></ul>
184. 193. DR. CLIFTON MEADOR NICELY SUMMARIZED THESE “NON-DISEASES” <ul><li>ROENTGENOLOGIC-OVERINTERPRETATION SYNDROMES </li></ul><ul><li>CONGENITALLY ABSENT-ORGAN SYNDROMES (“Non-functioning” kidneys and gall bladders that are not there) </li></ul><ul><li>OVERINTERPRETATION-OF-PHYSICAL FINDINGS SYNDROMES </li></ul>
185. 194. Conditions among patients who “ain’t broke, so don’t fix them” <ul><li>Adie’s pupil </li></ul><ul><li>Café au lait spots </li></ul><ul><li>Campbell de Morgan spots </li></ul><ul><li>Non-dwarfism </li></ul><ul><li>Pregnancy </li></ul><ul><li>Pityriasis rosea </li></ul><ul><li>Silent gallstones </li></ul><ul><li>Ptosis of the kidney (in normotensive) </li></ul><ul><li>“ Letter-reversal” in a </li></ul><ul><li>4-year old </li></ul><ul><li>Umbilical hernia in </li></ul><ul><li>infancy </li></ul>11. Symptomless hypotension 12. Symptomless hiatus hernia 13. Symptomless hyperuricemia 14. Symptomless colonic diverticulae 15. Small degrees of stable scoliosis 16. Non-Cushing’s disease 17. Symptomless hypokalemia in thiazide- treated hypertensives who are not taking digitalis
186. 195. THREE WAYS OF PICKING UP THERAPY <ul><li>YOUR OWN UNCONTROLLED CLINICAL EXPERIENCE (INDUCTION METHOD) </li></ul><ul><li>FORMAL RANDOMIZED CLINICAL TRIALS (DEDUCTION METHOD) </li></ul><ul><li>RECOMMENDATIONS OF OTHERS (ABDICATION OR SEDUCTION METHOD) </li></ul>
187. 196. SELECTING SPECIFIC TREATMENT <ul><li>THE HYPOTHETICO-DEDUCTIVE METHOD IS </li></ul><ul><li>PREFERRED FOR SELECTING SPECIFIC TREATMENTS </li></ul><ul><li>THE BEST INFORMATION ON WHETHER A GIVEN TREATMENT DOES MORE GOOD THAN HARM TO PATIENTS WITH A GIVEN DISORDER IS THE </li></ul><ul><li>RESULTS OF A RANDOMIZED CLINICAL TRIAL </li></ul>
188. 197. SIX GUIDES TO DISTINGUISH USEFUL FROM USELESS OR EVEN HARMFUL THERAPY <ul><li>Was the assignment of patients to treatments really randomized ? </li></ul><ul><li>Were all clinically relevant outcomes reported ? </li></ul><ul><li>Were the study patients recognizably similar to your own ? </li></ul><ul><li>Were both clinical and statistical significance considered ? </li></ul><ul><li>Is the therapeutic maneuver feasible in your practice? </li></ul><ul><li>Were all the patients who entered the study accounted for at its conclusion </li></ul>
189. 198. SIX GUIDES TO DISTINGUISH USEFUL FROM USELESS OR EVEN HARMFUL THERAPY <ul><li>Guides 1 & 6 deal mostly with validity </li></ul><ul><li>(Are the article’s conclusions true?) </li></ul><ul><li>Guides 2, 3, & 5 deal mostly with </li></ul><ul><li>applicability (Are the article’s conclusions </li></ul><ul><li>relevant to your own patients?) </li></ul><ul><li>Guide 4 deals with both validity (statistical significance) and applicability (clinical significance) </li></ul>
190. 199. Clinically relevant outcomes in a randomized trial of clofibrate in the prevention of coronary heart disease PLACEBO CLOFIBRATE Average change in serum cholesterol (%) +1 - 9 Non-fatal myocardial infarctions per 1000 subjects 7.2 5.8 Fatal and nonfatal myocardial infarctions per 1000 subjects 8.9 7.4 Total deaths per 1000 subjects 5.2 6.2
191. 200. WERE THE STUDY PATIENTS RECOGNIZABLY SIMILAR TO YOUR OWN ? <ul><li>The clinical and socio-demographic status of study patients must be described in sufficient detail </li></ul><ul><li>The study patients should be at least roughly similar to patients in your practice. </li></ul>
192. 201. WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? <ul><li>CLINICAL SIGNIFICANCE </li></ul><ul><ul><li>refers to the importance of a difference in clinical outcomes between treated and control patients. </li></ul></ul><ul><ul><li>usually described in terms of the magnitude of a result. </li></ul></ul><ul><li>STATISTICAL SIGNIFICANCE </li></ul><ul><ul><li>tells us whether the conclusions the authors have drawn are likely to be true (regardless of whether or not they are clinically important). </li></ul></ul>
193. 202. WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? <ul><li>If the difference is statistically significant, is it clinically significant as well ? </li></ul><ul><li>If the difference is not statistically significant, was the trial big enough to show a clinically important difference if it had occurred ? </li></ul><ul><li>“ CLINICAL SIGNIFICANCE ” GOES BEYOND ARITHMETIC AND IS DETERMINED BY CLINICAL JUDGMENT . </li></ul>
194. 203. WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? <ul><li>An article that reports on a randomized double-blind </li></ul><ul><li>clinical trial comparing a new drug ( Drug A ) with an </li></ul><ul><li>identical appearing placebo ( Drug B ) for the control of an </li></ul><ul><li>important clinical disorder. </li></ul><ul><li>Based on the results, the authors of the article will have </li></ul><ul><li>drawn one of two conclusions: either Drug A is better </li></ul><ul><li>than Drug B or Drug A is no better than Drug B. </li></ul>
195. 204. Comparing the conclusions drawn from a clinical trial with the true state of affairs w x y z TP=true positive; FP= false positive; FN= false negative; TN= true negative THE CLINICAL TRIAL IS THE DIAGNOSTIC TEST The true state of affairs Drug A is better than drug B Drug A is no better than drug B Conclusion drawn from a clinical trial Drug A is better than drug B TP Correct FP Error Drug A is no better than drug B Error FN Correct TN
196. 205. Naming the erroneous conclusions from a clinical trial w x y z The true state of affairs Drug A is better than drug B Drug A is no better than drug B Conclusion drawn from a clinical trial Drug A is better than drug B TP Correct (1-  = power) FP Type I error (risk of making this error=  =P value) Drug A is no better than drug B Type II error (risk of making this error=  ) FN Correct TN
197. 206. WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? <ul><li>The relationships between Type I and Type II errors are used in both planning and interpreting randomized trials. </li></ul><ul><li>In planning such a trial, investigators can decide beforehand just how great a risk they are willing to run of drawing erroneous conclusions of both sorts </li></ul><ul><li>Most authors decide to set the false-positive (  ) risk at .05 and the false-negative (  ) risk at .20 – conventional levels of statistical significance. </li></ul>
198. 207. WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? <ul><li>In other clinical situations, esp. in the growing number of cases in which clinicians want to find out whether a new treatment is not better than, but as good as; a standard treatment of higher toxicity or cost, the false-negative risk may be set lower. </li></ul>
199. 208. IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? <ul><li>One of the landmark U.S. Veterans Administration trials of whether treating hypertension would prevent fatal and nonfatal target organ damage. </li></ul><ul><li>In this trial, patients with and without prior target organ damage (to the heart, brain, eye, kidney, or major vessels) at entry were randomized to receive either active anti-hypertensive drugs or identical appearing placebos, and the clinical course were observed over the next 3 years for the subset of men who entered before the age of 50 with diastolic blood pressures between 90 and 114 </li></ul>
200. 209. Occurrence of death, stroke, or other major complications How might these benefits be expressed in terms of clinical significance ? Patient status at entry Adverse event rates Placebo P Active RX A Prior target organ damage .22 .08 No prior organ damage .10 .04
201. 210. Occurrence of death, stroke, or other major complications These relative risk reductions mean that the risk of death, stroke, or other complications of hypertension was reduced by almost two-third through active treatment Patient status at entry Adverse event rates Relative Risk Reduction RRR Placebo P Active A (P – A) -----------= RRR P Prior target organ damage .22 .08 .22 - .08 ----------- = 64% .22 No prior organ damage .10 .04 .10 - .04 ----------- = 60% .01
202. 211. IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? <ul><li>YARDSTICK FOR Relative Risk Reduction (RRR) </li></ul><ul><ul><li>Relative risk reductions of  50% almost </li></ul></ul><ul><ul><li>always, and of  25% often, are </li></ul></ul><ul><ul><li>considered to be clinically significant. </li></ul></ul><ul><ul><li>A quick and useful measure of clinical </li></ul></ul><ul><ul><li>significance. </li></ul></ul>
203. 212. Occurrence of death, stroke, or other major complications The decimal form of absolute risk reduction is foreign to most clinicians Patient status at entry Adverse events Absolute risk reduction ARR Placebo P Active A RRR P – A = ARR Prior target organ damage .22 .08 64% .22-.08=.14 No prior organ damage .10 .04 60% .10-.04=.06
204. 213. IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? <ul><li>For easy interpretation of absolute risk reduction, we take the reciprocal of it. </li></ul><ul><li>The reciprocal of the absolute risk reduction is the number of patients we need to treat in order to prevent one complication of their disease </li></ul><ul><li>This measure of clinical significance is called the number needed to treat (NNT) </li></ul>
205. 214. Occurrence of death, stroke, or other major complications Patient status at entry Adverse events Number Needed to Treat (NNT) Placebo P Active A RRR ARR 1 ----- = NNT ARR Prior target organ damage .22 .08 64% .14 1 ---- = 7 .14 No prior organ damage .10 .04 60% .06 1 ---- = 17 .06
206. 215. The effect of different baseline risks and relative risk reductions on the number needed to treat Baseline risk (with no treatment) Relative risk reduction on treatment 50% 40% 30% 25% 20% 15% 10% .9 .6 .3 2 3 7 3 4 8 4 6 11 4 7 13 6 8 17 7 11 22 11 17 33 .2 .1 .05 10 20 40 13 25 50 17 33 67 20 40 80 25 50 100 33 67 133 50 100 200 .01 .005 .001 200 400 2000 250 500 2500 333 667 3333 400 800 4000 500 1000 5000 667 1333 6667 1000 2000 10000
207. 216. The effect of different baseline risks and relative risk reductions on the number needed to treat <ul><li>Conclusions: </li></ul><ul><ul><li>When the absolute baseline risk of the bad clinical </li></ul></ul><ul><ul><li>outcome is high, even modest relative risk </li></ul></ul><ul><ul><li>reductions generate gratifyingly small NNT. </li></ul></ul><ul><ul><li>Small changes in the absolute baseline risk of a </li></ul></ul><ul><ul><li>rare clinical event lead to big changes in the </li></ul></ul><ul><ul><li>numbers of patients we need to treat in order to </li></ul></ul><ul><ul><li>prevent one. </li></ul></ul>
208. 217. IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? <ul><li>Sample case </li></ul><ul><ul><li>When Hill and his colleagues performed their randomized </li></ul></ul><ul><ul><li>trial of home-versus-hospital care for patients with </li></ul></ul><ul><ul><li>suspected myocardial infarction (in the days before </li></ul></ul><ul><ul><li>thrombolytic therapy), they observed a 6-week case- </li></ul></ul><ul><ul><li>fatality rate of 20% among the 132 patients who were </li></ul></ul><ul><ul><li>randomized to be treated at home. This rate was not </li></ul></ul><ul><ul><li>statistically significantly different from the 6-week case- </li></ul></ul><ul><ul><li>fatality rate of 18% they documented among the other 132 </li></ul></ul><ul><ul><li>patients who were randomized to treatment in hospital </li></ul></ul>
209. 218. IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? <ul><li>Can we conclude that it was safe in those days to treat such coronary patients at home ? </li></ul><ul><li>Was this trial big enough to show a clinically significant difference (say a 25% or 50% better among hospitalized coronaries) if it did occur ? </li></ul>
210. 219. Was the trial big enough to show a relative risk reduction of  25% if it had occurred ? Observe rate of events in the experimental group .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .05 Observed rate of events in the control group .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .05 14 27 68 391 11 18 38 110 1057 14 25 54 185 4889 11 18 33 78 326 13 22 44 112 635 11 16 28 57 165 1524 13 20 35 75 250 6349 10 15 24 43 99 402 12 17 28 53 132 722 13 20 33 65 180 1607 10 15 22 38 79 254 11 16 25 44 98 381 12 18 28 50 121 634 13 19 30 57 1296 10 13 20 33 64 196 4537 10 14 20 34 71 261 10 14 20 35 78 371 10 13 20 34 80 589 12 17 30 74 1245
211. 220. Was the trial big enough to show a relative reduction of  50% if it had occurred ? Observed rate of events in the experimental group .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .08 .06 .04 .02 Observed rate of events in the control group .98 .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .08 .06 .04 .02 14 24 50 165 5803 12 19 37 102 921 14 26 58 236 12 19 38 108 995 10 15 27 63 256 12 21 41 116 1059 16 29 66 268 13 22 43 120 1082 11 17 30 68 270 13 22 42 119 1059 11 17 30 66 260 13 22 42 113 987 11 16 28 62 239 13 20 38 102 867 10 15 26 55 205 12 18 33 86 699 13 22 45 160 10 15 26 64 482 11 17 32 102 254 2017 14 25 66 131 453 12 20 44 76 179 1313 10 16 31 47 87 274 12 22 30 47 97 561
212. 221. IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? <ul><li>The trial needed 261 patients per group to be confident that it had not missed a risk reduction of 25% in the 6-week case-fatality rate of coronary patients treated in hospital </li></ul><ul><li>The trial needed 45 patients per group (50%) </li></ul><ul><li>The trial was too small to reject a 25% improvement, but large enough to reject a 50% improvement in the 6-week case-fatality rates of coronary patients treated in hospital </li></ul>
213. 222. IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? <ul><li>95% CONFIDENCE INTERVAL OR CONFIDENCE LIMIT ON RISK REDUCTION, NNT OR OTHER MEASURE OF EFFICACY </li></ul>
214. 223. IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED ? <ul><li>This is a Swedish Co-operative Stroke Study carried out to </li></ul><ul><li>determine whether patients with cerebral infarcts might have </li></ul><ul><li>fewer subsequent strokes if they took aspirin. Placebos were </li></ul><ul><li>given to 252 controls patients (n C ), and 18 of these </li></ul><ul><li>(p C = 18 / 252 = .07) had a subsequent nonfatal stroke. Aspirin </li></ul><ul><li>was given to 253 experimental patients (n E ), of whom 23 (p E = </li></ul><ul><li>23 / 253 = .09) had a recurrent nonfatal stroke. The results </li></ul><ul><li>certainly did not favor aspirin. There was an absolute increase of </li></ul><ul><li>.02 between the two groups, generating a relative risk increase </li></ul><ul><li>(rather than reduction) of 29%. </li></ul>
215. 224. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL Control (Placebo) Experimental (aspirin) Patients with recurrent strokes n c = 252 p c = .07 n E = 253 p E = .09 Absolute risk reduction = p c – p E = .07 - .09 = -.02 Relative risk reduction = (p c – p E ) / p c = .02 / .07 = -29%
216. 225. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL = = From to
217. 226. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL <ul><li>The result appears quite definitive in terms of excluding any possible benefit from aspirin </li></ul><ul><li>Based on confidence interval analysis </li></ul><ul><ul><li>(- .02 - .05 =) - .07, generating a relative risk increase of recurrent stroke from aspirin of (- .07 / .07 =) – 100%, support the prior suspicion that aspirin cannot be beneficial in this situation. </li></ul></ul>
218. 227. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL <ul><ul><li>(-.02 + .05 =) + .03, generating a relative risk reduction of recurrent stroke from aspirin of (.03 / .07 =) + 43% </li></ul></ul><ul><ul><li>If we believe that a risk reduction of 30% or more would be clinically significant, we cannot regard the Swedish study as definitively excluding a benefit of aspirin. </li></ul></ul>
219. 228. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL <ul><li>In summary, when an article draws a negative conclusion </li></ul><ul><li>about a treatment (because P  .05), you can focus on the </li></ul><ul><li>upper end of the confidence interval for the relative risk </li></ul><ul><li>reduction, for this place the treatment in the most </li></ul><ul><li>favorable light. </li></ul><ul><li>If this upper boundary lies below what you’d consider to </li></ul><ul><li>be the smallest clinically significant risk reduction, you are </li></ul><ul><li>reading about a definitively negative trial </li></ul>
220. 229. CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL <ul><li>If, on the otherhand, this upper end of the </li></ul><ul><li>confidence interval includes clinically important </li></ul><ul><li>relative risk reductions, the trial hasn’t ruled </li></ul><ul><li>them out and cannot be regarded as definitively </li></ul><ul><li>negative. </li></ul>
221. 230. IS THE THERAPEUTIC MANEUVER FEASIBLE IN YOUR PRACTICE <ul><li>The therapeutic maneuver has to be described in sufficient detail for readers to replicate it with precision. </li></ul><ul><li>Must be clinically sensible </li></ul><ul><li>Must be available </li></ul><ul><li>Must note whether the authors avoid two specific biases in its application </li></ul>
222. 231. IS THE THERAPEUTIC MANEUVER FEASIBLE IN YOUR PRACTICE <ul><li>Contamination (in which control patients accidentally receive the experimental treatment </li></ul><ul><li>Cointervention ( the performance of additional diagnostic or therapeutic acts on experimental but not the control patients) </li></ul>
223. 232. WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION <ul><li>What can a reader do when outcomes are not </li></ul><ul><li>reported for missing subjects ? </li></ul><ul><li>Best case/worse case approach </li></ul><ul><ul><li>Arbitrarily assign a bad outcome to all missing </li></ul></ul><ul><ul><li>members of the group which fared better, and </li></ul></ul><ul><ul><li>good outcome to all missing members of the </li></ul></ul><ul><ul><li>group that fared worse. </li></ul></ul>
224. 233. WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? <ul><li>What can a reader do when outcomes are </li></ul><ul><li>not reported for missing subjects ? </li></ul><ul><li>Best case/worse case approach </li></ul><ul><li>If this maneuver fails to cancel the statistical </li></ul><ul><li>or clinical significance of the results, the reader </li></ul><ul><li>can accept the study’s conclusions </li></ul>
225. 234. WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? <ul><li>Example </li></ul><ul><ul><li>A cohort of 123 morbidly obese patients was </li></ul></ul><ul><ul><li>studied 19 – 47 months after surgery. </li></ul></ul><ul><ul><li>Success was defined as having lost more than </li></ul></ul><ul><ul><li>30% of excess weight. Only 103 patients </li></ul></ul><ul><ul><li>(84%) could be located. In these, the success </li></ul></ul><ul><ul><li>rate of surgery was 60/103 (58%) </li></ul></ul>
226. 235. WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? <ul><li>Solution: </li></ul><ul><li>Best case success rate = (60 + 20) / 123 = 65% </li></ul><ul><li>Worse case success rate = 60 / 123 = 49% </li></ul><ul><li>Thus the true rate must have been 49 and 65% </li></ul>
227. 236. Treatment Clinical question: How does treatment change the course of disease? <ul><li>Usually the effects of treatment are much less obvious and most interventions require research to establish their value </li></ul><ul><li>Specific interventions must do more good than harm among patients who use them (efficacious and effective) </li></ul><ul><li>The most desirable method for measuring efficacy and effectiveness is that of the randomized controlled trial </li></ul>
228. 237. Treatment Clinical question: How does treatment change the course of disease? <ul><li>Intervention studies </li></ul><ul><li>Clinical trials </li></ul><ul><li>Controlled trials </li></ul><ul><li>Uncontrolled trials </li></ul><ul><li>Concurrent control </li></ul>
229. 238. Treatment Clinical question: How does treatment change the course of disease? <ul><li>Types of clinical trial (according to purpose) </li></ul><ul><li>Prophylactic trials, e.g. immunization, contraception </li></ul><ul><li>Therapeutic trials (drug treatment, surgical procedures </li></ul><ul><li>Safety trials (side-effects of drug) </li></ul><ul><li>Effectiveness trials (theoretical, use, and extended use effectiveness of contraceptive methods) </li></ul><ul><li>Risk factor trials (proving etiology of disease) </li></ul><ul><li>Efficiency trials </li></ul>
230. 239. Treatment Clinical question: How does treatment change the course of disease? <ul><li>Phases of Clinical Trials </li></ul><ul><li>Phase I Clinical Trials </li></ul><ul><li>experimental animals used to establish that the new </li></ul><ul><li>agent is effective and suitable for human use </li></ul><ul><li>1 st phase in humans – pharmacologic and toxicologic </li></ul><ul><li>studies </li></ul><ul><li>Phase 2 Clinical Trials </li></ul><ul><li>assess the effectiveness of the drug or device </li></ul><ul><li>determine the appropriate dose </li></ul><ul><li>investigate its safety </li></ul>
231. 240. Treatment Clinical question: How does treatment change the course of disease? <ul><li>Phase 3 Clinical Trials (Classical phase) </li></ul><ul><li>performed on patients with consent </li></ul><ul><li>carried out mostly on hospital in-patients </li></ul><ul><li>assess the effectiveness, safety and continued use of the drug/device </li></ul><ul><li>Phase 4 Clinical Trials </li></ul><ul><li>a trial in normal field or program setting </li></ul><ul><li>reassess effectiveness, safety, acceptability and continued use of the drugs </li></ul>
232. 241. Natural history of a disease and prognosis Clinical question: What are the consequences of having a disease ? <ul><li>Prognosis </li></ul><ul><li>is a prediction of the future course of disease following its onset </li></ul><ul><li>Natural history of disease </li></ul><ul><li>refers to the stages of a disease </li></ul>D x time P R E- S Y M P T O M A T I C CLINICAL DISEASE EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME RECOVERY DISABILITY DEATH
233. 243. Natural history of disease and prognosis Clinical question: What are the consequences of having a disease ? <ul><li>Prognostic factors </li></ul><ul><li>are conditions that are associated with a given outcome of the disease </li></ul><ul><li>Risk factors Prognostic factors </li></ul><ul><li>events being counted is a variety of consequences </li></ul><ul><li>the onset of disease of disease are counted </li></ul><ul><li>predict low probability describe relatively </li></ul><ul><li>events frequent events </li></ul>
234. 244. Outcomes of Disease (the Five Ds) Death A bad outcome if untimely Disease A set of symptoms, physical signs, and laboratory abnormalities Discomfort Symptoms such as pain, nausea, dyspnea, itching, and tinnitis Disability Impaired ability to go about usual activities at hoe, work, or recreation Dissatisfaction Emotional reaction to disease and its care, such as sadness or anger
235. 245. Natural history of disease and prognosis Clinical question: What are the consequence of having a disease ? <ul><li>Multiple prognostic factors and prediction rules </li></ul><ul><li>A combination of factors may give a more precise prognosis than each of the same factors taken one at a time </li></ul><ul><li>Clinical prediction rules estimate the p