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Diagnostic test Evaluation
- 1. Screening and Diagnostic Testing Amita kashyap Sr Prof and Head P S M S.M.S. Medical College, Jaipur
- 2. Objective of the Session • How to the assess the Performance of a Diagnostic/ Screening Test –To judge Accuracy of a Diagnostic/ Screening Test –To learn about Predictive Values of a Diagnostic/ Screening Test –To measure the extent to which the likelihood of the disease is changed by the result of a Diagnostic Test
- 3. Complex Process of Clinical Reasoning • Practice of Clinical Medicine is “Artful Application” of Science • Howsoever trained a Clinician may be he has to apply “Learned Probability Estimates” to reach to the “Final Diagnosis” • The purpose of a Dx Test is to move the estimated probability of the disease towards either end of the probability scale – ‘no disease to disease’
- 4. Patient Profile • A 45 yr old man’s BP was 140/86 during routine check up for job, he was obese. • His father died of MI at 65 yrs of age • Total S Cholesterol (non fasting) was 242 mg/ dl • No other abnormality • Physician asked him to come after 2 weeks; fasting, for further testing • Repeat total S Cholesterol (fasting) was 198 mg/ dl • Physician’s decision to treat by drugs changed!!
- 6. Levels Features Individual Individual variability Measurement variability Population Genetic variability btw individuals Environmental variability Measurement variability Sample Manner of Sampling Size of Sample Measurement variability Levels of Variability
- 7. Sources of variability features Individual characteristics Diurnal variation Factors like Age, diet and exercise Environmental like season and temperature Measurement characteristics Poor calibration of instrument Inherent lack of precision of the instrument Observers misreading or recording Potential Sources of Variability
- 8. Total Population Sample Frame Sampling scheme Eligible Subjects Inclusion Criteria Exclusions Subjects asked to participate Informed consent Participates Lost to Follow Up Participants complete the study
- 9. Technique of taking Sample
- 10. Size of Sample 6 3.5 8.5 8.5 5 12.5 5 7 5.8 8 10 9
- 11. Bias – a threat to Validity • Bias The systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease (Selection bias, Information bias, Berkson’s bias) Randomization reduces the chance difference (Random Variability) between the groups
- 12. Confounding • Confounding In a Study to assess whether Factor ‘A’ is a cause of Disease ‘B’, a third factor, Factor ‘X’ may be a confounder if the following are true 1. Factor ‘X’ is a known Risk factor of Disease ‘B’ 2. Factor ‘X’ is associated with Factor “A”, but is not a result of Factor ‘A’
- 13. How to address Confounders There are two accepted methods for dealing with potential confounder 1.Consider them in design by matching on potential confounder OR by restricting the sample to limited levels of potential confounders 2.Evaluate confounder in analysis by stratification or by using multivariate analysis (multiple logistic regression)
- 14. A. Causal B. Due to confounding Characteristic under study (increased Cholesterol) Disease (MI) Characteristic under study (increased Cholesterol) Disease (MI) Factor X (Obesity) Observed association Observed association Effect of Confounding on Association
- 15. Increased Cholesterol Mayocardial Infarction Confounding A potential confounder must satisfy two conditions: 1. Association with the disease of interest in the absence of exposure 2. Association with the exposure but not as a result of exposure Obesity
- 16. Confounding = High total S. Cholesterol 67% has MI 3% has MI 87% has high cholesterol 13% has high cholesterol 60% has high cholest erol 40% has high cholestero
- 17. Patient Profile : • A 54 year old high school teacher got her physical examination for insurance. She had no complaints- the hot flashes she had experienced a year ago had resolved without treatment. • On physical examination, comprising breast, pelvic (including PAP smear), and rectal examination; nothing remarkable found. • Physician recommended Mammogram; which indicated abnormality, hence radiologist suggested breast biopsy. She was referred to a surgeon; on physical examination breast was normal. • Based on Mammographic abnormality however; both surgeon and radiologist agreed for FNA under radiologic guidance for abnormal breast. • FNA specimen revealed cancer cells and patient was scheduled for further surgery next week.
- 18. 0.3 13 20 40 60 80 100 64 After Positive FNA result Without lump Before Mammogram After positive Mammogram Probability of Breast Cancer (Percent) Palpable Lump on PE Prior to Mammogram Schematic Diagram of the “Estimated Probability” of Ca Br in a 54 yr old women- 1) without palpable Breast Mass, 2)after A Positive Mammogram and 3) Following a +ve FNA Probability of Br Ca is as high as 1%, if patient’s sister/ mother had been previously diagnosed with CA Breast
- 19. Purpose of Screening and diagnostic tests • To distinguish between people who have the disease and those who do not. • Hence quality is critical
- 20. Question is! How to assess :- The quality of a newly available screening and diagnostic tests So as to make:- Reasonable decisions about their use and interpretation.
- 21. Before using a test to distinguish, It is important to understand how characteristics are Distributed in populations!
- 22. 0 5 10 15 20 25 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Number of Subjects Induration in mm Distribution of tuberculin reaction.
- 23. 0 20 40 60 80 100 120 <110 110 120 130 140 150 160 170 180 >180 Number in thousands systolic blood pressure in mm Hg Distribution of systolic blood pressure
- 24. importance of “cut off” value Test Result Persons with disease Persons without the disease
- 25. Test Result Call these persons “negative” Call these persons “positive” “Cut off” Threshold
- 26. Test Result Call these persons “negative” Call these persons “positive” with the disease without the disease True Positives Some definitions ...
- 27. Test Result Call these persons “negative” Call these persons “positive” with the disease without the disease False Positives
- 28. Test Result Call these persons “negative” Call these persons “positive” with the disease without the disease True negatives
- 29. Test Result Call these persons“negative” Call these persons “positive” with the disease without the disease False negatives
- 33. Thank You
Editor's Notes
- To assess the quality of newly available screening and diagnostic tests to make reasonable decisions about their use and interpretation.
- Howsoever trained a Clinician may be he has to apply “Learned Probability Estimates” to reach to the “Final Diagnosis” – no two individuals react alike and behave alike under the abnormal conditions of the disease
- Premature CVD was defined as an event <51 years for men or <56 for women. According to National cholesterol education program S Ch > 240 mg/dl is an indication for drug tt, 200 – 239 is boderline where diet and lifestyle corrections are considered
- To minimize individual variability – repeat measure and take average, measure 24 hr, for measurement variability – standardize instrument-caliberation, repeat measure and take average, technique (fasting or not); same laboratory type of analyser
- Berkson’s bias – hospital patients usually has more than one disease therefore false associations can be registered Selection Bias Information Bias Confounding : (can be quantified) otherwise evaluation of bias is subjective Likelihood of 1) the presence of bias and 2) its potential magnitude of effect
- Unacceptability Bias e.g. – in a case control study regardless of disease status participants may under report eating high fat diet thinking its not good making it difficult for the researchers to identify an association- bias towards the null hypothesis
- 87% obese has high cholesterol while only 13% of non obese had it There are two accepted methods for dealing with potential confounder Consider them in design by matching on potential confounder or by restricting the sample to limited levels of potential confounders Evaluate confounder in analysis by stratification or by using multivariate analysis (multiple logistic regression)
- Diagnostic procedures may include all approaches to gather clinical information can be regarded as test – history taking, physical examination, X-ray, Lab Test etc Clinical decision making is based on probability… The purpose of a diagnostic test is to move the estimated probability of the presence of a disease toward either end of the probability scale based on new meaningful information that will alter subsequent treatment / diagnostic plans 0.3% is the expected prevalence of Br Ca in that community for a women of 40 yrs and above It was estimated 13% with positive Mammogram, the radiologist may have predicted slightly lower or higher proportion based on some particular mammographic findings Positive FNA raised the probability to 64%- again based on certain characteristics of her specimen, such as appearance of nucleus, nuclear/cytoplasmic ratio the proportion could have been a little less or more. Further different pathologist may reach to different conclusions- some clearly says cancer cells present and other may say specimen is suspicious for Ca Cells!!! If the patient’s sister or mother had been previously diagnosed with CA Br patient’s likelihood of having breast cancer prior to any test could have been as high as 1%, Tests is used to estimate the probability of an outcome
- Purpose of Screening and diagnostic tests- To understand how a disease is transmitted and develops and to provide appropriate and effective health care, it is necessary to distinguish between people in the population who have the disease and those who do not. This is an important challenge, both in the clinical arena, where patient care is the issue, and in the public health arena, where secondary prevention programs that involve early disease detection and intervention are being considered and where etiologic studies are being conducted to provide a basis for primary prevention. Thus, the quality of screening and diagnostic tests is a critical issue. Regardless of whether the test is a physical examination, a chest X-ray, an electrocardiogram, or a blood or urine assay, the same issue arises: How good is the test in separating populations of people with and without the disease in question?
- A large group centers on the value of 0 mm—no indurations—and another group centers near 20 mm of indurations. This type of distribution, in which there are two peaks, is called a bimodal curve. The bimodal distribution permits the separation of individuals who had no prior experience with tuberculosis (people with no induration, seen on the left) from those who had prior experience with tuberculosis (those with about 20 mm of induration, seen on the right). Although some individuals fall into the “gray zone” in the center, and may belong to either curve, most of the population can be easily distinguished using the two curves. Thus, when a characteristic has a bimodal distribution, it is relatively easy to separate most of the population into two groups (e.g., ill and not ill, having a certain condition or abnormality and not having that condition or abnormality).
- In general, however, most human characteristics are not distributed bimodally. Figure shows the distribution of systolic blood pressures in a group of men. In this figure there is no bimodal curve; what we see is a unimodal curve—a single peak. Therefore, if we want to separate those in the group who are hypertensive from those who are not hypertensive, a cutoff level of blood pressure must be set above which people are designated hypertensive and below which they are designated normotensive. No obvious level of blood pressure distinguishes normotensive from hypertensive individuals. Although we could choose a cutoff for hypertension based on statistical considerations, we would ideally like to choose a cutoff on the basis of biologic information; that is, we would want to know that a pressure above the chosen cutoff level is associated with increased risk of subsequent disease, such as stroke, myocardial infarction, or subsequent mortality. Unfortunately, for many human characteristics, we do not have such information to serve as a guide in setting this level. In either distribution—unimodal or bimodal—it is relatively easy to distinguish between the extreme values of abnormal and normal. With either type of curve, however, uncertainty remains about cases that fall into the gray zone.