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Matching in case control studies Yvan Hutin. Cases of acute hepatitis (E) by residence, Girdharnagar, Gujarat, India, 2008 Attack rate per 1,000 > 40.

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Presentation on theme: "Matching in case control studies Yvan Hutin. Cases of acute hepatitis (E) by residence, Girdharnagar, Gujarat, India, 2008 Attack rate per 1,000 > 40."— Presentation transcript:

1 Matching in case control studies Yvan Hutin

2 Cases of acute hepatitis (E) by residence, Girdharnagar, Gujarat, India, 2008 Attack rate per 1,000 > 40 30-39 20-29 >0-10 0 Water pumping station Leak Drain overflow

3 Risk of hepatitis by place of residence, Girdharnagar, Gujarat, India, 2008 3 Source of waterHepatitisNo hepatitisTotal Leaking pipes /overflowing drain1448,6948,838 No leakages / overflowing drain8912,43612,525 Total23321,13021,363 RR = 2.3, Chi Square= 41.1 df= 1. P < 0.001

4 Chipat river Attack rate of acute hepatitis (E) by zone of residence, Baripada, Orissa, India, 2004 0 - 0.9 / 1000 1 - 9.9 / 1000 10 -19.9 / 1000 20+ / 1000 Attack rate Underground water supply Pump from river bed

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 Acute hepatitis ControlTotal Drunk pipeline water 493134627 Did not drink pipeline water 45404449 Total538 1076 Adjusted odds ratio = 33, 95 % confidence interval: 23- 47 Consumption of pipeline water among acute hepatitis cases and controls, Baripada, Orissa, India, 2004

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: –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 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 –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 OR M-H = a i.d i ) / Ti] b i.c i ) / Ti] Mantel-Haenszel adjusted odds ratio

20 CasesControlsTotal Exposed112 Non exposed000 Total112 CasesControlsTotal Exposed000 Non exposed 112 Total112 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 No effect

21 CasesControlsTotal Exposed101 Non exposed011 Total112 CasesControlsTotal Exposed011 Non exposed 101 Total112 Matched analysis by set (Pairs of 1 case / 1 control) Discordant pairs –Cases and controls have different exposures –ads and bcs: input to the calculation Positive associationNegative association

22 OR M-H = a i.d i ) / Ti] b i.c i ) / Ti] The Mantel-Haenszel odds ratio...

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

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 CasesControlsTotal ExposedabL1 UnexposedcdL0 TotalC1C0T Odds ratio: ad/bc The old 2 x 2 table...

27 Controls ExposedUnexposedTotal Exposedefa Unexposed ghc TotalbdP (T/2) Odds ratio: f/g Cases... is difficult to recognize!

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

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 –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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