Clinical Epidemiology is a vital science for understanding, control and prevention of health and production problems encountered daily be clinicians.

1. Techniques in Clinical
Epidemiology
Dr. M.Senthil Murugan Research Scholar
Dr. BR Singh, Head Division of Epidemiology Indian Veterinary
Research Institute, Izatnagar & Director CCS NIAH, Baghpat

2. Clinical Epidemiology ?
• The research discipline concerned with applying
epidemiologic methods to questions relevant to the
practice of medicine at the individual or herd/ flock
level.
• Epidemiology – study of diseases in natural habitat,
away form controlled environment
• Clinical Epidemiology - the study focusing on the
sorts of questions asked in practice of medicine

3. Field / Clinical Epidemiology
Velez et al., 2013

4. Where it is used?
Issue Question
Normality /
Abnormality
•What are the limits of normality?
•What abnormalities are associated with having the disease?
Diagnosis •How accurate are the diagnostic tests and strategies used to find the disease?
Frequency /
Occurrence
•Case definition for a disease, how common are each of the findings
•Host, spatial and temporal distribution of disease
Risk /
Prevention
•What factors are associated with the likelihood of contracting disease?
Prognosis •What are the consequences of having a disease?
•What factors are associated with an increased or decreased likelihood of
recovering form disease?

5. Where it is used?
Treatment /
Control
•How effective the therapeutic strategy and how does it change the future
course of the disease?
•How risk and rate of spread can be reduced – useful tools for diagnosis,
treatment, control and prevention?
Cause •What is the etiologic agent? Its life cycle? Its pathogenicity and virulence?
•Factors determine susceptibility and resistance of individuals to the disease?
•Predisposing population conditions to outbreaks?
Source /
Transmission
•Source and reservoir mechanism and period of communicability for causative
agent?
•Spread and route of infection to susceptible?
Cost •What is the impact of a disease in personal and economic terms?

6. Interactions of Clinical Epidemiology

7. Evidence Based Medicine
• The relationship between epidemiology and clinical
medicine has been formalized in the practice of evidence
based medicine (EBM)
• The process of systematically finding, appraising, and
using contemporaneous research findings as the basis for
clinical decisions

8. Steps in EBM
Identify one or more
clinically important
information needs and
convert them in to
answerable questions.
Track down, with max
efficiency the best evidence
with which to answer the
above questions
Summarize and critically
appraise the evidence –
Scientific validity and
applicability
Apply the results of this
appraise to patient care
Evaluate your performance
at answering the questions

9. Attributes for Defining the
limits of normality
• Medical decision making – SOAP (Subjective data, objective data,
assessment and plan)
• Clinical data are nominal (Categorical), ordinal (ranking) or interval
(Continuous)
• Validity (Accuracy) – degree to which the a measurement reflects
the true status of what is being measured
• Reliability ( Precision) – measure of repeatability and reproducibility
of clinical measurement
• Coefficient of Variation (CV) – express the precision of Clinical
measurements
▫ S.D / Mean = Percent variation of set of measurements around their
mean

10. Normal or Gaussian Distribution

11. Defining normality
• First Data is expressed as frequency distribution.
• Set the Measures of Central Tendency – mean, median, mode.
• Determine Dispersion – range, SD, Percentile, Deciles, Quartile.
Normal or Gaussian Distribution
• Naturally occurring distributions – Two tailed tests of significance.
• Assessed by Skewedness and Chi Square goodness-of-fit test.
• Critical values (Cut offs) are set: Mean ± 1.96 SD : 95% normal range & 5 % outside
normal range.
• Test of Significance (one tailed) is applied : Mean ± 1.645 SD.
Limitations
• Outside of range includes False positive and false negatives.
• Only denotes the random variations, not others variations.

12. Techniques for evaluation
of Diagnostic Test
• Medical decision making is critical.
• Diagnostic testing – diseased are differentiated from non-diseased and/ or those with other
diseases with similar symptoms.
• Screening–in healthy subjects- identification of unrecognized diseases in apparently healthy
ones.
• Gold standard test - definitive test – determines whether disease is truly present or not. The
best example is Post Mortem Examination.
• Sensitivity – proportion of true positives detected .
• Specificity - proportion of true negatives detected .
• False Positive Rate – likelihood of a positive test result in a patient known to be free of
disease.
• False Negative rate - likelihood of a negative test result in a patient known to have disease.

13. 2x2 table– uni-variate analyses

14. Evaluation of Diagnostic Test
• Predictive Value of the test: probability of the test result that reflects the
true disease status of an individual.
▫ Positive predictive value: Probability of disease in an animal with a positive
(abnormal) test result.
▫ Negative predictive value: Probability that an animal does not have the
disease when the test result is negative(normal).
• Likelihood ratio: compares the proportion of animals with or without
disease, in relation to their test results.
▫ LR + = proportion of affected individuals test positive / proportion healthy
individuals test positive.
▫ LR - = proportion of affected individuals test negative / proportion healthy
individuals test negative.

15. Techniques for evaluation of Diagnostic tests
Test Parameter
being evaluated
How measured How expressed
Validity 2 x 2 Contingency table
1. Sensitivity
2. Specificity
3. Positive and negative predictive
values
4. Accuracy
Optimum Cutoff
Response Operating
Characteristic (ROC) Curve
1. Positive and negative Cutoff
value
Comparison of
tests
1. Fixed Cutoff : Pretest/ Post
test curve
2. Continuous Variable : (ROC)
Curve
1. Posterior probability / prior
probability
2. Likelihood ratio at different levels
of test, Area under the curve
Clinical utility
1. True positive rate/ false
positive rate
2. False negative rate/ true
negative rate
3. Decision analysis
1. Likelihood ratio for a positive test
2. Likelihood ratio for a negative
test
3. Testing and treatment thresholds

16. Techniques in Clinical Study designs
Experimental studies •allocation of individuals is under control of investigator
•Randomization – Statistical procedures
•symmetry of potential unknown confounders
•strongest empirical evidence
Randomized Controlled
Clinical Trial (RCT)
•prospective, analytical, experimental study
•randomly allocated to two or more treatment groups
•outcomes the groups are compared after follow-up time
•clinical efficacy of preventive and therapeutic procedures
Randomized Cross-Over
Clinical Trial
•prospective, analytical, experimental study
•Individuals - chronic condition – 2 or more treatment groups
•washout period - switched to the other treatment for the same period
•Susceptible to bias
Randomized Controlled
Laboratory Study
•laboratory environment
•powerful tools -all extraneous factors other than those of interest can be
controlled
•Interaction of factor – clinical setting – not applicable

17. Clinical Study designs contd…
Observational Studies: •allocation of factors is not under control of investigator
•self-selected or are "experiments of nature”
•provide weaker empirical evidence
•large confounding biases - unknown association between a factor and
an outcome
•preliminary evidence - hypotheses - stronger experimental studies
Cohort Study
(Incidence,
Longitudinal Study)
•prospective, analytical, observational study
•follow-up period - association between that exposure and an outcome
•lack of control over risk assignment
•zero time bias
• more expensive
Case-Control Study • retrospective, analytical, observational study
• Cases compared with controls
• Association measured by ODDS RATIO
• initial, inexpensive evaluation of risk factors
• rare condition - long induction periods
• Potential for bias

18. Clinical Study designs contd…
Ecologic (Aggregate)
Study
•Aggregate data on risk factors –
• compare different populations groups- identify associations
•inferred from the group level
•likelihood of an ecologic fallacy
Cross-Sectional
(Prevalence Study) Study
•descriptive study - at one point in time
•relationship between diseases and other factors
•lack -information on timing of exposure and outcome
relationships
Case Series: •descriptive, observational study of a series of cases
•manifestations, clinical course, and prognosis of a condition
•used as a source of hypotheses - further studies
•most common study type - clinical literature.
Case Report •description of a single case
•manifestations, clinical course, and prognosis
•little empirical evidence to the clinician – single case

19. Relative Merits of Clinical research
Designs
Study design Limitations Best application
Uncontrolled clinical trial •Time
•Ethical consideration
•No comparison group
•Prognosis with or without
treatment
Non randomized controlled
clinical trial
•Time
•Ethical consideration
•Bias in selection of comparison
group
•Prognosis with or without
treatment
•Evaluation of new treatments
Randomized controlled clinical
trial
•Time
•Ethical consideration
•Prognosis with or without
treatment
•Evaluation of new treatments
Experimental disease •Time
•Availability of animal or other
animal models
•Cost
•Proving relationship between risk
and causal factors of disease
•Pathogenic mechanisms

20. Relative Merits of Clinical Research Designs
Study design Limitations Best application
Case Report •Temporal relation ship
•Bias in case selection
•Statistical validity
•Detailed description of uncommon
diseases
•Surveillance
Case series •Temporal relation ship
•Bias in case selection
•Frequency of findings in disease
Prevalence survey •Temporal relationship
•Measures prevalence not incidence
•Evaluation of diagnostic tests
•Incrimination of risk factors
•Outbreak investigation
Case Control •Temporal relation ship
•Bias in selection of comparison group
•Evaluation of diagnostic tests
•Incrimination of risk factors
•Outbreak investigation
•Rare diseases or diseases of long
latency

21. Bias in clinical Observation
• Selection bias
– Occurs when selection and/or follow-up procedures lead to study
group differences in determinants of outcome other than the one
under study
• Measurement bias
– Occurs when measurements are imprecise and/or the methods of
measurement are dissimilar among study groups
• Confounding bias
– Occurs when two factors are associated (“travel together”) and the
effect of one is confused with or distorted by the effect of another)
– Due to selection or by chance

22. Bias in Clinical observation
• Ecological (Aggregation) Bias (Fallacy):
• association observed between variables representing group averages is mistakenly taken
to represent the actual association that exists between these variables for individuals.
• Reader Bias -errors of interpretation made during inference by the user or reader of clinical
information.
• tendency is to accept information that supports pre-conceived opinions.
• Zero Time Bias: individuals are found and enrolled in such a fashion that unintended
systematic differences occur between groups at the beginning of the study.
• individuals are found and enrolled in such a fashion that unintended systematic differences
occur between groups at the beginning of the study.
• stage of disease, confounder distribution - Cohort studies.

23. Placebo Effect
• Any medical intervention – non specific, psychological, psycho-physiologic
therapeutic effect.
• Used for presumed specific therapeutic effect – but is without specific activity for
the condition treated.
• May be favorable or unfavorable.
• Used as control in clinical trials and understanding the mechanism of work.
• In animal context – effect of human contact ( visual & tactile) on animal health.
• Forms a investigator bias rather than biologically mediated effect in animals.
• Clinical trials
▫ placebos as controls
▫ second control without placebos

24. Techniques to Control Bias
• Randomization:
▫ Control groups are randomly allocated
▫ Balance the distribution of other variables may be outcome related
▫ Guarantees the validity of the statistical tests
▫ Block randomization and stratification
• Blinding or masking
▫ Procedures that prevent the study participants, caregivers, outcome assessors from
knowing the intervention received
▫ Single, double, triple and quadrupled blinded RCT (Stanley, 2007)
• Allocation concealment (Viera, et al., 2007)
▫ Procedure for protecting randomization process so that the treatment can be
allocated is not known before the patient is entered in to the study
▫ Sequentially numbered opaque sealed envelopes SNOSE method is used

25. BLINDING

26. Techniques to control
confounding
• Adjusting – using adjusted rates in confounder
• Matching
▫ Frequency matching – groups are divided so that they contain
same proportion of same confounding variable
▫ Individual matching – each case in group matched with a control –
respect to variable
• Multivariate Methods:
▫ The Mantel-Haenszel procedure
▫ Summary of odds ratio is used
▫ stratifying data with respect to potential confounders.

27. The Mantel-Haenszel procedure
• calculate the crude odds ratio.
• calculate the adjusted odds ratio.
• decide whether the difference between the crude and the adjusted values
is 'big' (this is somewhat arbitrary); if the answer is 'yes', then there is
confounding and the adjusted odds ratio should be used, if there is no
interaction.
• assess if the stratum-specific odds ratios are homogeneously distributed
across the strata (e.g., using either Woolf's statistic or the Breslow-Day
statistic); if not, there is interaction, and the sub tables should not be
collapsed.

28. Techniques for validity
• Internal validity
– The degree to which the results of a study are correct for the sample of
patients being studied.
▫ Truth within the study.
▫ Results not likely due to bias or confounding.
▫ All studies are flawed some degree – but not crucial to study results.
• External validity (Probability of being Generalized)
▫ The degree to which the results of an observation hold true in other
settings -Truth beyond study (Demicheli V, et al., 2013).
▫ Study population similar to reader population.

29. Measures of commonness of disease
• Measuring the frequency of disease events
▫ Assessing the risk of contracting a disease
▫ Its cause
▫ Prognosis and response to treatment
• Frequency is expressed as proportions or rates
▫ Cases in numerator and population at denominator
▫ Rate = affected/ affected + unaffected
▫ Ratio = Affected / unaffected
▫ Vital statistics rates – indirect measure - commonness
▫ Morbidity rate (Prevalence, Incidence, Attack rate) – direct
measure - commonness

30. Vital statistics in Veterinary Medicine
Rate Calculation Measure of
Crude live birth
rate
(No of live birth/ Avg. population)* 10n Population increment due to
natural causes
General Fertility
Rate
(No of live birth/ Avg. of females of Rep
age)* 10n
Index of overall reproductive
performance
Crude Death rate (No of death/ Avg. population)* 10n Population loss due to natural loss

31. Morbidity / Mortality rates
Rate Calculation Measure of
Attack Rate (No of individuals during an outbreak /
population at risk at the beginning of the
outbreak) )* 10n
Identifying risk factors for specific
disease in outbreak investigation
Incidence rate (No of new cases of disease over a time
interval / avg. population at risk during
the time interval) )* 10n
•Monitoring the course of epidemic
•Cohort study – measure of effect
of suspected or known risk factors
Prevalence rate (No of existing cases of disease at a
point of time / population at risk during
same point of time)* 10n
•Static measure of risk of having a
particular disease
•Case control study – effect of
suspected or known risk factors
Case Fatality
rate
No of deaths from a specified cause/
Total no of cases of same disease
•Determine prognosis of disease
•Severity of the disease

32. Techniques for Risk Assessment and
Prevention
• Uses – Prediction, Diagnosis, Cause, Prevention
• Risk Factors – increased likelihood of an event occurring.
• Comparison of Risks – strength of association between risk factor and
outcome
• Statistical significance of this association
• Univariate analysis – 2 x 2 table
▫ Relative risk and Odds ratio are calculated
• Multivariate analyses
▫ Number of potential risk factors and outcome of interest
▫ Multivariate logistic regression analyses
▫ Y=a + b1X1 + b2X2 + b3X3 + e

33. Measures of effect in studies of risk of disease
Expression Clinical Question Formula
Relative Risk (Risk
Ratio)
How many times more likely are
exposed individuals to become diseased
relative to unexposed?
Incidence in exposed/
incidence in unexposed
Attributable Risk
(Risk Difference)
What is the incidence of disease
attributable to exposure?
Incidence in exposed –
Incidence in unexposed
Population
Attributable risk
What is the incidence of disease in a
population associated with the
occurrence of Risk factor?
ARP = AR * Prevalence
Population
Attributable Fraction
What fraction of disease in a population
is attributable to exposure to a risk
factor?
AFP = ARP / Total incidence in
population
Odds Ratio (Cross
Product Ratio)
Odds of case exposed / odds of control
exposed ( in case control Studies)
a * d / b * c
Smith R.D, 2006

34. Meta-analysis Technique
• Statistical analysis of data pooled from several studies to integrate
findings
• Evaluation of diagnostic tests
• Cost benefit analysis for diagnostic techniques and treatment
• Assessment of magnitude of health problems
• Strong evidence for efficacy of treatment
• Common approach for provide a weighted estimate – odds ratio,
relative risk are used
• Homogeneity of data from different studies using chi square test
• Fixed effect model and random effect model – based on effect of
treatment

35. Conclusion
Clinical epidemiology competences includes
• Understanding of how bias and chance can affect the accuracy of observations in individual
patients.
• Evaluation of the validity of original observations over diagnosis, prognosis, treatment and
prevention.
• Knowing the strengths and weaknesses of randomized clinical trials, case-control studies, cohort
studies (prospective and retrospective) and meta-analysis.
• Using practical strategies to judge the validity of clinical evidence synthesis (i.e. reviews).
• Comprehension of the meaning, uses and limits of the statistic power, the values of ‘p’ and the
confidence interval, relative risk, attributable risk.
• Knowing how to measure the patients’ preferences.
• Comprehension and usage of the sensibility analysis and the cost-effectiveness analysis.