# 7251 Introduction to Cancer Epidemiology. Part III by sammyc2007

VIEWS: 39 PAGES: 29

• pg 1
```									      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

1
Case Control Study Design

Exposed
Diseased
(Cases)
Not Exposed
Target
Population
Exposed
Not Diseased
(Controls)
Not Exposed

2
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)

3
Odds Ratio Calculation

Outcome
Exposure       Cases        Controls
Exposed          A             B
Not Exposed       C             D

Odds of exposure for cases       A/C
= Odds Ratio
Odds of exposure for controls    B/D       (estimates the
relative risk)
4
Comparing Odds Ratios and Relative Risks

Outcome
Exposure    Cases   Controls
Exposed      70       300      370
Not Exposed    30       700      730
100     1000      1100

OR = AD/BC = 5.44     RR = Ie/In = 4.41

5

   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

6
Summary of Strengths and Limitations of
Prospective Cohort and Case-Control Studies
Prospective Cohort                  Case-Control
Strengths:                              Strengths:
 Opportunity to measure risk factors    Useful for rare disease
before disease occurs                  Relatively inexpensive
 Can study multiple disease             Relatively quick results
outcomes
 Can yield incidence rates as well   Limitations:
as relative risk estimates           Possible bias in measuring risk
factors after disease has
Limitations:                           occurred
 Useful for rare disease             Possible bias in selecting control

 Relatively inexpensive
group
 Identified cases may not
 Relatively quick results
represent exposure of all cases
7
Randomized Clinical Trials
(RCT)

The Gold Standard
Cohort Study

8
Schematic diagram of a clinical trial
Study
Population                                Non-participants
Participants

Randomization
Treatment arm                              Control arm
Intervention or                               Control
new treatment

Improved   Not Improved
Improved              Not
Improved
9
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

10
Factorial Design
   Two or more treatments are evaluated
simultaneously in the same set of subjects using
varying combinations of treatments
Randomization

Treatment A                        Placebo

Treatment B    Placebo           Treatment B       Placebo

11
How do we evaluate whether
cancer studies are valid?

Understand bias and confounding
Testing for a true association

   Examine the methodology for bias

   Examine the analysis for confounding

   Examine the results for statistical significance

13
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

14
Confounding

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

15
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.
16
Testing for Confounding
1. Calculate the crude rate

2. Calculate a rate adjusted for the confounding
variable

3. 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%)
17
1980 U.S.    Expected
Cancer     Population                    Standard     Number of
Age       Deaths       at risk       ASR
Deaths
Population
(1)        (2)      (1) / (2) = (3)      (4)        (3) x (4) = (5)
0-18         5        5,000     1.00 per 1000     60,500,000      60,500
19-64        10       25,000     0.40 per 1000 140,300,000        56,120
65+         100       15,000    6.67 per 1000     25,700,000     171,419
Total       115      45,000          xxx          226,500,000    288,039

(115 / 45,000) x 1000    Not equal (288,039 / 226,500,000) x 1000
2.56 per 1,000                          1.27 per 1,000

AGE IS A CONFOUNDER FOR DEATH FROM CANCER
18
Evaluating Statistical Significance

   The probability that you would get your results
by chance alone is the p-value
   A low p-value ( < 0.05 ) says that chance is not
   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

19
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

20
How do we assess whether
associations between cancer and
risk factors are causal?

Understand criteria for causality
To Show Cause

   Chronic disease and complex conditions
require the use of Hill’s Postulates
1.   Strength of association      5.   Plausibility
2.   Consistency of               6.   Coherence
association                  7.   Experiment
3.   Specificity of association   8.   Analogy
4.   Temporality

22
How much of the morbidity and
mortality from cancer might be
prevented by interventions?

Understand the impacts of education
and screening programs
   Validity
Principles of Screening
   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
24
Calculating Measures of Validity
True Diagnosis

Test Result       Disease        No Disease     Total
Positive           a            b          a+b
Negative          c             d          c+d
Total             a+c          b+d       a+b+c+d

Sensitivity = a/(a+c)        Positive Predictive Value = a/(a+b)
Specificity = d/(b+d)        Negative Predictive Value = d/(c+d)

25
Example: Breast Cancer Screening
Mammogram
Results      Disease      No Disease            Total

Positive            132           983           1,115
Negative             45         63,650      63,695
Total              177         64,633      64,810

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%
26
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
27
   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
28
End

```
To top