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4/20/2015
Clinical Epidemiology
Jeffrey A. Summers, MD FACP
Professor, Department of Medicine
Quillen College of Medicine
ETSU
Definition
Causes, distribution, and effects of health and disease in populations
The Investigation and control of the distribution and determinants of disease
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
Easy Definition
All That Statistics Stuff
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Outline
Testing in Populations
Risk - Relative, absolute
Kinds of Clinical Research Studies
P Values
Populations
True
False
TP
FP
TN
FN
+
-
Populations
Spin and Snout
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Populations
Sensitivity, Specificity, Positive and Negative
Predictive Values all reflect ACCURACY in a
PARTICULAR POPULATION
Populations
Sensitivity - Accuracy in SICK
Specificity - Accuracy in WELL
PPV - Accuracy in those with a POSITIVE TEST
NPV - Accuracy in those with a NEGATIVE TEST
Sensitivity
Accuracy in Sick = How many sick people have
a positive test
Specificity
Accuracy in Well = How many well people have a negative test
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Positive Predictive Value
Accuracy in Positive Test = How many people
with positive test are sick
Negative Predictive Value
Accuracy in Negative Test = How many people with a negative test are well
Predictive Value is Dependent on Prevalence
Calculate Sensitivity and Specificity
You do a study of PET scanning for CAD. 1000
patients are tested, with 400 positive tests. Of
those with positive tests, 390 had abnormal
angiograms. Of the 600 with negative tests, 20
had abnormal angiograms. What is the
sensitivity, specificity, and positive and negative
predictive values?
Calculate S/S and Predictive values
Sens: There were 410 patients with disease, 390 had
positive tests. Sensitivity = 95%
Spec: There were 590 patients without disease, 580 had
negative tests. Specificity = 98%
PPV: There were 400 positive tests, 390 with disease. PPV
= 97.5
NPV: There were 600 negative tests, 580 without disease.
NPV = 96.7%
Prevalence of CAD is 410/1000 or 41%
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Bismarck Problem
You are the Director of the Health Department in Bismarck,
ND, lucky you. The City Council has heard that HIV testing
is 99% sensitive and 99% specific and would like to require
HIV testing for all couples about to marry. The prevalence
of HIV in Bismarck is 1 in 1000. What is the likelihood that
a screened person with a positive test actually has HIV?
Solution
Assume 10,000 screened. In that group, there will be 10
with HIV. With 99% sensitivity, 9.9 of those will test positive
(99% of 10). Of the 9990 that do not have HIV, 99% will
have negative tests (99% specific). 1% will have positive
tests, or 99.9 people (1% of 9990).
Rounding, 110 positive tests, 10 true positives. Positive
Predictive Value is then 10/110 or 9%.
There is a 9% probability that a person in that population
with a positive test actually has HIV
Risk/Risk Reduction
Absolute Risk Reduction
Relative Risk Reduction
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Absolute Risk Reduction
There is a risk of something happening. You do
something to modify the risk. The difference
(subtraction) between the original risk and new
risk is the absolute risk reduction.
Relative Risk Reduction
There is a risk of something happening. You do
something to reduce the risk. The difference
between the old and new risk (subtraction)
DIVIDED by the old risk is the relative risk
reduction
Clopidogrel
Early study of clopidogrel showed a reduction
in mortality in MI with the med from 3% to 2%.
Absolute risk reduction = 3-2 = 1%
Relative risk reduction = (3-2)/3 = 33%
Which was used in ads, and pushed to change
guidelines???
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Number Needed to Treat
Number Needed to Treat (NNT) is the
reciprocal of the Absolute Risk Reduction
(1/ARR)
1% ARR means number needed to treat to
benefit one patient is 100.
Cost Benefit
Cost Benefit is the NNT times the cost, or the
cost to benefit one patient.
In our clopidogrel example, cost is 100*cost of
treating with clopidogrel for 1 year, which at
$3.50 per pill was $127,750 to benefit one
patient.
Clinical Research Studies
Cohort - start with a group that gets an intervention, follow
outcomes. Can be prospective or retrospective.
Case Control - start with group with the outcome, match
controls, and look at differences. Better for rare conditions.
Cross-sectional (survey, prevalence)
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Question
In an "outcomes" analysis of coronary bypass surgery, health services researchers
identify charts of all patients diagnosed with three vessel disease at three major clinical
centers during the past ten years. These patients are separated into those who initially
were treated surgically and those who were initially treated medically, with surgery used if
medical treatment was unsuccessful. Aggregate results for mortality and a variety of
other outcome variables were compiled for each group, to produce prognostic profiles for
those initially treated medically vs. those who received immediate surgery. This study
was a:
A.
B.
C.
D.
E.
prospective cohort study
retrospective cohort study
cross-sectional survey
hospital-based case-control study
controlled clinical trial
Question
In a study examining the relationship between oral contraceptives and
bacteriuria, you follow women who do and do not use oral contraceptives over
a three-year period, and find that 70 of the 500 individuals who use OC
acquired bacteriuria, while 150 of 3000 individuals who don’t use OC acquired
bacteriuria.
Is this a cohort or case-control study?
What is the absolute risk conferred by the OCs?
What is the relative risk conferred by the OCs?
Answers
It is a cohort study.
Risk of bacteriuria with OCs is 70/500= 0.14
Risk without OCs is 150/3000= 0.05
Absolute risk conferred is 0.14-0.05= 0.09 (9%)
Relative risk conferred is 0.14/0.05= 2.8
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P Values
Lots of incorrect definitions
NOT the probability the conclusion is wrong
NOT the probability that the null hypothesis is
true
P Value
P value is the Probability, given the null
hypothesis is true, that you would have gotten
the results you did.
In other words, if the null hypothesis is true,
there is only a n% chance that the study would
have come out the way it did
P Value
p<0.05 Cutoff is historical. Story behind it is
hysterical.
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