Case-Control Study Design

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Case-Control Study Design

• Two groups are selected, one of people with the
  disease (cases), and the other of people with the
  same general characteristics but without the
  disease (controls)
• Compare the past exposures of both groups

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Case Control Study Design
Exposed
Diseased (Cases)
Not Exposed
Target Population
Exposed
Not Diseased (Controls)
Not Exposed
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Case-Control Study Design

• Limitations
• Cannot yield incidence rates because subjects are
  selected on outcome
• An estimate of the ratio of incidence rates or
  risks (RR) is obtained by calculating an odds
  ratio (OR)

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Odds Ratio Calculation
Outcome
Controls
Cases
Exposure
B
A
Exposed
Not Exposed
D
C
Odds Ratio
A / C
Odds of exposure for cases
Odds of exposure for controls
B / D
(estimates the relative risk)
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Comparing Odds Ratios and Relative Risks
Outcome
Controls
Cases
Exposure
370
300
70
Exposed
Not Exposed
730
700
30
1100
1000
100
OR AD/BC 5.44
RR Ie/In 4.41
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Stating your results

• OR 5.44
• Those with the disease are 5.44 times as likely
  to have had the exposure compared to those
  without the disease
• RR 4.41
• Those with the exposure are 4.41 times as likely
  to develop the disease compared to those without
  the exposure

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Summary of Strengths and Limitations of
Prospective Cohort and Case-Control Studies
Case-Control
Prospective Cohort

• Strengths
• Useful for rare disease
• Relatively inexpensive
• Relatively quick results

• Strengths
• Opportunity to measure risk factors before
  disease occurs
• Can study multiple disease outcomes
• Can yield incidence rates as well as relative
  risk estimates

• Limitations
• Possible bias in measuring risk factors after
  disease has occurred
• Possible bias in selecting control group
• Identified cases may not represent exposure of
  all cases

• Limitations
• Useful for rare disease
• Relatively inexpensive
• Relatively quick results

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Randomized Clinical Trials(RCT)
The Gold Standard Cohort Study
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Schematic diagram of a clinical trial
Study Population
Non-participants
Participants
Randomization
Treatment arm
Control arm
Intervention or new treatment
Control
Improved
Not Improved
Not Improved
Improved
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Crossover Design

• Subjects are randomized to a sequence of two or
  more treatments
• Each subject serves as his own control

Group Sequence Period 1 Washout Period 2
I A , B A B
II B , A B A
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Factorial Design

• Two or more treatments are evaluated
  simultaneously in the same set of subjects using
  varying combinations of treatments

Randomization
Placebo
Treatment A
Placebo
Treatment B
Placebo
Treatment B
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How do we evaluate whether cancer studies are
valid?

• Understand bias and confounding

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Testing for a true association

• Examine the methodology for bias
• Examine the analysis for confounding
• Examine the results for statistical significance

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Examine the study design for Bias

• Selection Bias
• Errors in the process of identifying the study
  population and selecting the subjects
• Information/Observation Bias
• Errors in measurements of exposure or disease
  status

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Confounding

• Confounding is an apparent association between
  disease and exposure caused by a third factor not
  taken into consideration

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Examples of Confounders

• Study A found an association between gambling and
  lung cancer. The study may be confounded by
  smoking.
• Study B found a larger crude death rate in
  Florida than in Alaska. The rate may be
  confounded by differences in the population age
  structure.

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Testing for Confounding

• Calculate the crude rate
• Calculate a rate adjusted for the confounding
  variable
• Compare the two measures
• The two measures will be different if the
  variable is a confounder (in practice, when the
  crude and adjusted measures differ by at least
  10)

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Expected Number of Deaths
1980 U.S. Standard Population
Population at risk
ASR
Cancer Deaths
Age
(3) x (4) (5)
(1) / (2) (3)
(4)
(2)
(1)
60,500
60,500,000
1.00 per 1000
5,000
5
0-18
0.40 per 1000
56,120
140,300,000
25,000
10
19-64
171,419
25,700,000
6.67 per 1000
15,000
100
65
288,039
xxx
45,000
115
Total
226,500,000
Crude Rate (115 / 45,000) x 1000 2.56 per 1,000
Age-Adjusted Rate (288,039 / 226,500,000) x 1000
1.27 per 1,000
Not equal
AGE IS A CONFOUNDER FOR DEATH FROM CANCER
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Evaluating Statistical Significance

• The probability that you would get your results
  by chance alone is the p-value
• A low p-value ( lt 0.05 ) says that chance is not
  likely to explain your results
• A 95 confidence interval (CI) is the range of
  values in which the true value will be found 95
  of the time
• Large samples yield small confidence intervals
• Small samples yield large confidence intervals

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Evaluating Results

• RR 1 No difference in disease between exposed
  and unexposed groups
• OR 1 No difference in exposure between cases
  and controls

• Examples
• RR 1.8 (1.6, 2.0) is statistically significant
• RR 1.8 (0.8, 2.9) is not statistically
  significant
• OR 0.7 (0.6, 0.8) is statistically significant
• OR 0.7 (0.4, 1.2) is not statistically
  significant

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How do we assess whether associations between
cancer and risk factors are causal?

• Understand criteria for causality

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To Show Cause

• Chronic disease and complex conditions require
  the use of Hills Postulates

1. Strength of association
2. Consistency of association
3. Specificity of association
4. Temporality
5. Biologic gradient

1. Plausibility
2. Coherence
3. Experiment
4. Analogy

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How much of the morbidity and mortality from
cancer might be prevented by interventions?

• Understand the impacts of education and screening
  programs

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Principles of Screening

• Validity
• Sensitivity correctly identify those with
  disease
• Specificity correctly identify those without
  disease
• Predictive Value proportion of correct
  positive tests
• - Predictive Value proportion of correct
  negative tests
• Reliability ability of test to give consistent
  results
• Yield amount of unrecognized disease brought to
  treatment due to screening

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Calculating Measures of Validity
True Diagnosis
Total
Disease
Test Result
No Disease
ab
b
a
Positive
c
cd
d
Negative
abcd
bd
ac
Total
Positive Predictive Value a/(ab) Negative
Predictive Value d/(cd)
Sensitivity a/(ac) Specificity d/(bd)
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Example Breast Cancer Screening
Mammogram Results
Total
No Disease
Disease
1,115
983
132
Positive
63,695
63,650
45
Negative
64,810
64,633
177
Total
Sensitivity 132/177 74.6 Specificity
63,650/64,633 98.5 Positive Predictive Value
132/1,115 11.8 Negative Predictive Value
63,650/63,695 99.9
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Keys to Screening

• Sensitivity detect a sufficient number of
  preclinical cases to be useful
• Prevalence screen high-risk populations
• Frequency one-time screening does not allow for
  differences in individual risk or differences in
  onset
• Participation tests unacceptable to the target
  population will not be utilized
• Follow-up those with positive tests need to be
  provided with a plan of action

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Advice for Reading the Literature

• Identify the study design
• Understand how subjects are selected
• Understand how exposure is defined
• Evaluate potential bias and confounding
• Determine if the statistical evaluation is
  appropriate
• Make decisions about whether the outcome measures
  are statistically significant and/or clinically
  important
• Use good judgment

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End