Transcript Case-Control Studies

Design and Analysis
Techniques for CaseControl Studies
Instructor: 李奕慧
[email protected]
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Lecture Overview
1.
2.
3.
4.
5.
Case-Control Study
Example: ”Risk factors associated
with lung cancer in Hong Kong”
OR for multiple exposure levels
Confounding factors
Methods of Controlling (adjusting for)
confounders
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Epidemiologic Study Design

Analytical studies
Intervention
studies
 Clinical trials
Observational studies
 Cohort studies
 Case-control studies
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Case-control study
Exposed
Cases
Non-exposed
Study Population
Exposed
Controls
Non-exposed
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Selection of cases

Establish a strict diagnostic criteria for the
disease:
Examples:
 Type 1 diabetes in children: severe symptoms,
very high BG, marked glycosuria, and
ketonuria.
 Type 2 diabetes: few if any symptoms, Slightly
elevated BG, diagnosis “complicated”.
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Selection of cases

Population-based cases: include all subjects or
a random sample of all subjects with the disease
at a single point or during a given period of time
in the defined population:
 Danish

childhood diabetes register
Hospital-based cases:
All patients in a hospital department at a given
time
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Selection of Controls
Principles of Control Selection:
 Study base:
 Controls
can be used to characterise the distribution of
exposure

Comparable-accuracy
 Equal
reliability in the information obtained from cases
and controls  no systematic misclassification

Overcome confounding
 Elimination
of confounding through control selection
matching or stratified sampling
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Selection of Controls

General population controls:
 registries,
households, telephone sampling
 costly and time consuming
 recall bias
 eventually high non-response rate

Hospitalised controls:
 Patients
at the same hospital as the cases
 Easy to identify
 Less recall bias
 Higher response rate
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Ascertainment of Disease and
exposure status

External sources:
 Death
certificates, disease registries, Hospital
and physicians records etc.

Internal sources:
 Questionnaires
and interviews, information
from a surrogate (spouses or mother of
children), biological sampling( e.g. antibody)
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Bias in Case-Control studies

Selection bias
 Non-response
 Detection


bias
cases and controls are identified not independently of
the exposure
Observation bias
 Recall
Bias: Cases are more likely to remember
exposure than controls
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Strengths in Case-control
 Quick,
inexpensive
 Well-suited to the evaluation of
diseases with long latency period
 Rare diseases
 Examine multiple etiologic factors for a
single disease
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Limitations in Case-control
Case-control study
 Not rare exposure
 Incidence rates cannot be estimated
unless the study is population based
 Selection Bias and recall bias
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Risk factors associated with
lung cancer in Hong Kong
Lung Cancer 40 (2003) 131-140
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Chi-Square Tests
Value
Pearson ChiSquare
Asymp. Sig.
(2-sided)
df
0.257a
1
Exact Sig. (1sided)
Exact Sig. (2-sided)
.613
Risk Estimate
95% Confidence Interval
Value
Odds Ratio for Marital
(other / married)
.880
Lower
.535
Upper
1.446
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Multiple Exposure Levels
Exposure
level
Cases
Controls
OR
A1
B1
OR1
B2
B3
OR2
Low
A2
A3
Not exposed
C
D
Reference
High
Medium
OR3
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Multiple Exposure Levels
A significant (P<0.05) increasing trend in the OR was found between nonsmokers,
ex- and current smokers; and increasing amount of smoking
among the ever smokers.
Lung cancer.sav
Lung Cancer 40 (2003) 131/140
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smoking * case Crosstabulation
smoking
Total
nonsmoker
Count
% within case
exsmoker
Count
% within case
current smoker Count
% within case
Count
% within case
case
case
control
52
96
24.5%
45.3%
68
87
32.1%
41.0%
92
29
43.4%
13.7%
212
212
100.0%
100.0%
Total
148
34.9%
155
36.6%
121
28.5%
424
100.0%
Chi-Square Tests
Pearson Chi-Square
Value
48.212a
df
Asymp. Sig.
(2-sided)
2
.000
Likelihood Ratio
50.088
2
.000
Linear-by-Linear
Association
N of Valid Cases
42.734
1
.000
424
抽煙與罹患肺癌有
關，
Case中抽煙者佔較
高的比例(43% vs
13.7%)
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 60.50.
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Data > Select cases >
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只選smoking=2,
3的資料進行分析
Risk Estimate
95% Confidence Interval
Value
Odds Ratio for
1.443
smoking (exsmoker /
nonsmoker)
Lower
Upper
.908
2.293
1.249
.942
1.656
For cohort case = control
.865
.721
1.039
N of Valid Cases
303
For cohort case = case
Exsmoker 罹患肺癌
是nonsmoker的1.4
倍,
95%CI (0.9, 2.3)
Exsmoker與
nonsmoker罹癌機率
沒有顯著差異
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Confounding factors (干擾因素）
Confounder:
Variable is associated with both the
disease and the exposure variable.
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Method for control for confounders


1.
2.
3.
Study design:
restriction/ matching/ randomization
Statistical adjustment:
Standardization; e.g. age standardized (where
age is a confounder)
Stratified by confounder (Mantel-Haenszel test)
Incorporate the confounder into a regression
analysis as a covariate. (logistic regression
approach)
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Restriction

Example
研究主旨：二手煙(ETS, exposure)與罹患肺癌
(disease)的關係
confounder: 研究對象本身是否抽煙
為了避免干擾只分析ETS對nonsmoker的影響
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Stratified Analysis
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將性別當作分層(stratum)的因子
smoking * case * sex Crosstabulation
Count
sex
male
smoking
ex- and current smoker
nonsmoker
female
Total
smoking
ex- and current smoker
nonsmoker
Total
case
case
control
160
116
Total
276
52
212
13
96
212
6
148
424
19
106
119
113
119
219
238
Lung cancer2.sav
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Sex-Specific OR for smoking
Risk Estimate
sex
male
female
95% Confidence
Interval
Value Lower
Upper
2.55
1.68
3.85
Odds Ratio for smoking (ex- and
current smoker / nonsmoker)
N of Valid Cases
Odds Ratio for smoking (ex- and
current smoker / nonsmoker)
424
2.31
N of Valid Cases
238
0.85
6.30
可以將男士的OR與女士的OR合併嗎？
怎麼併？
Lung cancer2.sav
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Thank you!
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