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Matching in case control studies

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Presentation on theme: "Matching in case control studies"— Presentation transcript:

1 Matching in case control studies
Lecture notes Matching in case control studies Yvan Hutin

2 Cases of acute hepatitis (E) by residence, Girdharnagar, Gujarat, India, 2008
Attack rate per 1,000 > >0-10 Water pumping station Leak Drain overflow

3 RR = 2.3, Chi Square= 41.1 df= 1. P < 0.001
Risk of hepatitis by place of residence, Girdharnagar, Gujarat, India, 2008 Source of water Hepatitis No hepatitis Total Leaking pipes /overflowing drain 144 8,694 8,838 No leakages / overflowing drain 89 12,436 12,525 233 21,130 21,363 RR = 2.3, Chi Square= 41.1 df= 1. P < 0.001

4 Attack rate of acute hepatitis (E) by zone of residence, Baripada, Orissa, India, 2004
Underground water supply Pump from river bed / 1000 / 1000 / 1000 20+ / 1000 Chipat river

5 Case-control study methods, acute hepatitis outbreak, Baripada, Orissa, India, 2004
Cases All cases identified through active case search Control Equal number of controls selected from affected wards but in households without cases Data collection Reported source of drinking water Comment events Restaurants

6 Adjusted odds ratio = 33, 95 % confidence interval: 23- 47
Consumption of pipeline water among acute hepatitis cases and controls, Baripada, Orissa, India, 2004 Acute hepatitis Control Total Drunk pipeline water 493 134 627 Did not drink pipeline water 45 404 449 538 1076 Adjusted odds ratio = 33, 95 % confidence interval:

7 Key elements The concept of matching The matched analysis
Pro and cons of matching

8 Controlling a confounding factor
Stratification Restriction Matching Randomization Multivariate analysis

9 The concept of matching
Confounding is anticipated Adjustment will be necessary Preparation of the strata a priori Recruitment of cases and controls By strata To insure sufficient strata size If cases are made identical to controls for the matching variable, the difference must be explained by the exposure investigated

10 Consequence.... The problem: Is solved with another problem:
Confounding Is solved with another problem: Introduction of more confounding, so that stratified analysis can eliminate it.

11 Definition of matching
Creation of a link between cases and controls This link is: Based upon common characteristics Created when the study is designed Kept through the analysis

12 Types of matching strategies
Frequency matching Large strata Set matching Small strata Sometimes very small (1/1: pairs)

13 Unmatched control group
Cases Controls Bag of cases Bag of controls

14 Sets of cases and controls that cannot be dissociated
Matched control group Cases Controls Sets of cases and controls that cannot be dissociated

15 Matching: False pre-conceived ideas
Matching is necessary for all case-control studies Matching needs to be done on age and sex Matching is a way to adjust the number of controls on the number of cases

16 Matching: True statements
Matching can put you in trouble Matching can be useful to quickly recruit controls

17 Matching criteria Potential confounding factors Criteria
Associated with exposure Associated with the outcome Criteria Unique Multiple Always justified

18 Risk factors for microsporidiosis among HIV infected patients
Case control study Exposure Food preferences Potential confounder CD4 / mm3 Matching by CD4 category Analysis by CD4 categories

19 Mantel-Haenszel adjusted odds ratio
ai.di) / Ti]  / Ti] OR M-H=

20 Matched analysis by set (Pairs of 1 case / 1 control)
Concordant pairs Cases and controls have the same exposure No ad and bc: no input to the calculation Cases Controls Total Exposed 1 1 2 Non exposed 0 0 0 Total 1 1 2 Cases Controls Total Exposed 0 0 0 Non exposed Total 1 1 2 No effect No effect

21 Matched analysis by set (Pairs of 1 case / 1 control)
Discordant pairs Cases and controls have different exposures ad’s and bc’s: input to the calculation Cases Controls Total Exposed 1 0 1 Non exposed 0 1 1 Total 1 1 2 Cases Controls Total Exposed 0 1 1 Non exposed Total 1 1 2 Positive association Negative association

22 The Mantel-Haenszel odds ratio...
S [(ai.di) / Ti] S [( / Ti] OR M-H=

23 …becomes the matched odds ratio
S Discordant sets case exposed S Discordant sets control exposed OR M-H=

24 …and the analysis can be done with paper clips!
Concordant questionnaire : Trash Discordant questionnaires : On the scale The "exposed case" pairs weigh for a positive association The "exposed control" pairs weigh for a negative association

25 Analysis of matched case control studies with more than one control per case
Sort out the sets according to the exposure status of the cases and controls Count reconstituted case-control pairs for each type of set Multiply the number of discordant pairs in each type of set by the number of sets Calculate odds ratio using the f/g formula Example for 1 case / 2 controls Sets with case exposed: +/++, +/+-, +/-- Sets with case unexposed: -/++, -/+-, -/--

26 The old 2 x 2 table... Cases Controls Total Exposed a b L1
Unexposed c d L0 Total C1 C0 T Odds ratio: ad/bc

27 ... is difficult to recognize!
Controls Exposed Unexposed Total Exposed e f a Unexposed g h c Total b d P (T/2) Odds ratio: f/g Cases

28 The Mac Nemar chi-square
(f - g) 2 (f+g) Chi2 McN=

29 Matching: Advantages Easy to communicate
Useful for strong confounding factors May increase power of small studies May ease control recruitment Suits studies where only one factor is studied Allows looking for interaction with matching criteria

30 Matching: Disadvantages
Must be understood by the author Is deleterious in the absence of confounding Can decrease power Can complicate control recruitment Is limiting if more than one factor Does not allow examining the matching criteria

31 Matching with a variable associated with exposure, but not with illness (Overmatching)
Reduces variability Increases the number of concordant pairs Has deleterious consequences: If matched analysis: reduction of power If match broken: Odds ratio biased towards one

32 Hidden matching (“Crypto-matching”)
Some control recruitment strategies consist de facto in matching Neighbourhood controls Friends controls Matching must be identified and taken into account in the analysis

33 Matching for operational reasons
Outbreak investigation setting Friends or neighbours controls are a common choice Advantages: Allows identifying controls fast Will take care of gross confounding factors May results in some overmatching, which places the investigator on “the safe side”

34 Breaking the match Rationale Procedure Matching may limit the analysis
Matching may have been decided for operational purposes Procedure Conduct matched analysis Conduct unmatched analysis Break the match if the results are unchanged

35 Take home messages Matching is a difficult technique
Matching design means matched analysis Matching can always be avoided

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