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Matching in case control studies

Matching in case control studies. Yvan Hutin. 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.

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Matching in case control studies

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  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 > 4030-3920-29 >0-10 0 Water pumping station Leak Drain overflow

  3. Risk of hepatitis by place of residence, Girdharnagar, Gujarat, India, 2008 RR = 2.3, Chi Square= 41.1 df= 1. P < 0.001

  4. Underground water supply Pump from river bed Attack rate of acute hepatitis (E) by zone of residence, Baripada, Orissa, India, 2004 Attack rate 0 - 0.9 / 1000 1 - 9.9 / 1000 10 -19.9 / 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. Consumption of pipeline water among acute hepatitis cases and controls, Baripada, Orissa, India, 2004 Adjusted odds ratio = 33, 95 % confidence interval: 23- 47

  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. Mantel-Haenszel adjusted odds ratio ai.di) / Ti] bi.ci) / 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 1 1 2 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 1 0 1 Total 1 1 2 Positive association Negative association

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

  23. …becomes the matched odds ratio SDiscordant sets case exposed SDiscordant 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) Chi2McN=

  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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