This document outlines a presentation on clinical epidemiology. It begins with an introduction to clinical epidemiology, noting that it was introduced in 1938 as a "new basic science for preventive medicine" and shifted its focus to individual patients in the 1960s. The document then defines clinical epidemiology as "the science of making predictions about individual patients by counting clinical events in similar patients." It discusses why clinical epidemiology is important for clinical decision making and avoiding bias. The rest of the document outlines topics to be covered, including uses of clinical epidemiology, sensitivity and specificity, predictive values, ROC curve analysis, and likelihood ratios.

1. DR.PRIYAMADHABA BEHERA
DR.GIRIDARA GOPAL

2. OUTLINE OF PRESENTATION
 INTRODUCTION
 USES OF CLINICAL EPIDEMIOLOGY
 SENSITIVITY AND SPECIFICITY
 PPV & NPV – PREVALENCE BASED
 ROC CURVE ANALYSIS
 LIKELIHOOD RATIOS

3. Introduction
answer clinical questions and
guide clinical decision making
Clinical medicine Epidemiology
Involved in methods
used to answer the
questions

4. Clinical epidemiology was introduced by –John Paul
in1938
According to him “a new basic science for preventive
medicine”
 The shift in the focus of clinical epidemiology from
community ecology to individual patients and groups
of patients took place in the 1960s

5. What is clinical epidemiology?
 The science of making predictions about individual
patients by counting clinical events in similar patients,
using strong scientific methods for studies of groups of
patients to ensure that the predictions are accurate
 Used as an aid to clinical decision making
Lead to valid conclusions by avoiding being misled by
systematic error (bias) and chance

6. Why learn about clinical
epidemiology?Clinical training has been oriented toward the mechanisms
of disease through study of biochemistry, anatomy,
physiology and other traditional basic science
Knowledge of the biology of disease should ordinarily be
considered hypothesis, to be tested by clinical research
b/o
-mechanism are only partly understood and many other
factors in the genetic, physical and social environments
affect outcome
eg:some antiarrhythmic drugs actually cause arrhythmias
Clinical epidemiology – foundation on which modern

7. Types of questions addressed
by
clinical epidemiology

8. Health Outcomes of clinical
epidemiologic studies
Clinical epidemiology can be studied directly only in intact humans
not in animals or part of humans, tissue cultures, cell membranes
and genetic sequences

9. Methods of clinical
epidemiology
1. Formulate question (hypothesis)
2. Choose study design
3. Choose study population and sample
from that
4. Collect and analyze data
5. Interpret results

10. Interpretation of diagnostic data
Comprise clinical data and paraclinical data
Clinical data comes from history and physical
examination
Paraclinical data is lab tests
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16. If we set cut off at 320 IU
100% of non infarct patient can be excluded but only
37% of infarct patient included
Will the same result be obtained in an another set of
360 patients ??
 80-160 range mild infarct
480 above is severe infarct
40 IU below is without infarct
Range of CK become larger with more patients
examined because of outliers
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18. Factors deciding rule in and rule out cut off :
1. If over diagnosis produced no harm we can relax rule
in cut off to exclude only 95% of patients.
2.If early diagnosis and therapy were essential for
satisfactory clinical outcome as in neonatal
screening , low rule out cut off is set. So virtually all
the babies are screened.
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22. Sensitivity ( positivity in disease) : proportion of
patients with the target disorder who have a positive
test result
Specificity ( Negativity in non diseased) : proportion
of non diseased who have negative test result
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24. Positive predictive value: proportion of patients with
positive test results who have the target disorder
Negative predictive value: proportion of patients with
negative test results who don’t have the target disease
disorder
Predictive values are not constant. They change with
the prevalence of disease
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31. Receiver operator
characteristic curve(ROC curve)
Express the relationship between sensitivity and
specificity
For a single test it helps to decide cut-off value for
diseases where many cut-off are possible
Useful when comparing two or more method
Accuracy of a test can be described area under the
curve, larger the area better is the test

32. A test that discriminates well the curve should lie in
upper left curve. little change in specificity unless test
reaches high sensitivity
Generally the cut-off point is near the shoulder point
until the clinician is interested to minimize false positive
or false negative

33. Receiver operator characteristic curve(ROC curve)

35. LIKELIHOOD RATIO
 Probability used to express the sensitivity, specificity
and predictive value
 Likelihood ratios expresses odds .

36. Likelihood Ratio
Bayes’ theorem no doubt helps to calculate PVs
But it is difficult to evaluate the posttest probs
without a pen and paper or a calculator
Another simple method of obtaining posttest
probabilities is by the use of ‘odds’ instead of
probabilities
The discriminating ability of a test between diseased
individuals and normal individuals can be measured
by an index called the Likelihood Ratio (LR)
Like the predictive values, LR is calculated
separately for positive test and negative tests

37. LIKELIHOOD RATIOA likelihood ratio is defined as the probability of a given level of a
test result in those with disease divided by the probability of that
same result in those without the disease.
 A likelihood ratio indicates how many times more (or less) likely
a test result of a given level is obtained in disease than in no
disease.
Likelihood ratios are a method of characterizing diagnostic tests.
They offer several important advantages compared to traditional
measures such as sensitivity and specificity
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38. CONTINUED
Disease
Positive(D+)
Disease
Negative(D-)
Test positive(T+) 70(a) 20(b) 90(a+b)
Test negative(T-) 30(c) 80(d) 110(c+d)
100(a+c) 100(b+d)
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LR+=Pr(T+/D+)/Pr(T+/D-)=sensitive/1-specificity=(70/100)/(20/100)=7/2=a/b
LR-=Pr(T-/D+)/Pr(T-/D-)=1-sensitive/specificity=(30/100)/(80/100)=3/8=c/d
Likelihood Ratio=LR+/LR- =(a/b)/(c/d)=ad/bc=OR
Likelihood ratio is numerically equal to odd ratio

39. References
 Clinical Epidemiology – THE ESSENTIALS – 3rd
Edition by Robert.H. Fletcher , Suzanne.W.Fletcher
 Clinical Epidemiology – A Basic science for Clinical
Medicine – 2nd
Edition by David.L.Sackett
 Modern Epidemiology – 2nd
Edition by Kenneth.J.
Rothman