CaseControl 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

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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

• Strength of association
• Consistency of association
• Specificity of association
• Temporality
• Biologic gradient

• Plausibility
• Coherence
• Experiment
• 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