1. Principles of case control studies
Part III
Matching
Many slides in this presentation are from the World Health Organization and
the European Programme for Intervention Epidemiology Training, thank you.
Piyanit Tharmaphornpilas MD, MPH
The International Field Epidemiology Training Program, Thailand

2. Sunbathe is a risk factor for being diabetes mellitus
Confounding
Hypothesis:
Sunbathe is a risk factor for being diabetes mellitus
Sunbathe Diabetes mellitus
Age is confounding factor!
need to be controlled
Age
Sunbathe
Diabetes mellitus
Reality :

3. How to control confounding factors
Randomisation
Restriction
Matching
Adjustment
Mutivariate analysis

4. Age Sunbathe Diabetes mellitus Because age is confounding factor, so
(In cohort study) Age of exposed and unexposed population should be comparable!
Then, effect of age on the study association will be taken off.
(In case-control) age of cases and controls should be comparable!
If a case ages 30, his control should age 30 too.
Age is confounding factor!
need to be controlled
Reality :
Age
Sunbathe
Diabetes mellitus

5. Types of matching Frequency matching Individual matching Large strata:
Controls are selected in proportion to the number of cases in each strata of the matching variable
Individual matching
Small strata :
For each case one or more controls are selected with the matching characteristics

6. Frequency matching
Controls are selected in proportion (%) to the number of cases in each strata of the matching variable
Age
15-24
25-34
35-44
45-54
>54
Total
Cases 30 20 10
100
Controls 60 40 20
200
The distribution of cases and controls is similar for age, and controls are no more representative of the not-ill population for age

7. Individual matching
For each case one or more controls are selected with the matching characteristics
No. Case Control1 Control2
1 age 30 age 30 ฑ 5 age 30 ฑ 5
2 age age 20 ฑ 5 age 20 ฑ 5
3 age 10 age 10 ฑ 5 age 10 ฑ 5
The distribution of cases and controls is similar for age, and controls are no more representative of the not-ill population for age

8. Matching : analysis If…. control enrolment is done by matching Then….
analysis should be adjusted for it (by strata)

9. S [(ai.di) / Ti] S [(bi.ci) / Ti] OR M-H=
Adjustment by Mantel-Haenszel
Using confounding (matching) variable as strata
S [(ai.di) / Ti]
S [(bi.ci) / Ti]
OR M-H=

10. Frequency matching : analysis
Stratified analysis on the frequency matching variable
Mantel Haenszel weigthed OR
Exposure Cases Controls Total
Strata 1
yes ai bi L1i
no ci di L0i
Total C1i C0i Ti
Strata j ....
S [(ai.di) / Ti]
S [(bi.ci) / Ti]
OR M-H =

11. Individual matching analysis Pairs of cases and controls
Exposed
Not Exposed
Exposed
C+/Ctr +
C+/Ctr -
Cases
C-/Ctr +
C-/Ctr -
Not Exposed
Pairs of cases and controls

12. Individual matching analysis Pairs of cases and controls
Exposed
Not Exposed e f
Exposed
Cases g h
Not Exposed
Pairs of cases and controls

14. Controls Exposed Not exposed Total
Exposed e f a
Not exposed g h c
Total b d T
Odds ratio: f/g C A S E

15. Controls Atherosclerosis Atherosclerosis risk in Communities study
Association between CMV infection and Carotid Atherosclerosis
Controls
CMV+
CMV-
CMV+
214 65
Atherosclerosis
CMV- 42 19
Cases and controls individually match paired by
Age group, sex, ethnicity, field center and date of exam
From: PD Sorlie et al, cytomegalovirus and carotid Atherosclerosis, Journal of Medical Virology, Vo 42, pp 33-37,1994

17. Why? ? We cannot analyze a matched case-control study
by unmatched method
Why? ?
Because matching process introduce selection bias
This selection bias is controllable by stratified analysis

18. Matching : advantages
When there is a potentially strong confounding variable
Tends to increase the statistical power
Logistically straightforward way to obtain a comparable control group

19. Matching: disadvantages
Difficult to find a matched control
Cannot assess the association between matching variables and outcome
Implies some tailoring of the selection of study groups to make them comparable (less representativeness)
Once is done cannot undone, risk of overmatching
No statistical power is gained if the matched variables are weak confounders

20. Don’t match (too much)
End of part III