Literature DB >> 29101596

Case-control matching: effects, misconceptions, and recommendations.

Mohammad Ali Mansournia1, Nicholas Patrick Jewell2,3, Sander Greenland4,5.   

Abstract

Misconceptions about the impact of case-control matching remain common. We discuss several subtle problems associated with matched case-control studies that do not arise or are minor in matched cohort studies: (1) matching, even for non-confounders, can create selection bias; (2) matching distorts dose-response relations between matching variables and the outcome; (3) unbiased estimation requires accounting for the actual matching protocol as well as for any residual confounding effects; (4) for efficiency, identically matched groups should be collapsed; (5) matching may harm precision and power; (6) matched analyses may suffer from sparse-data bias, even when using basic sparse-data methods. These problems support advice to limit case-control matching to a few strong well-measured confounders, which would devolve to no matching if no such confounders are measured. On the positive side, odds ratio modification by matched variables can be assessed in matched case-control studies without further data, and when one knows either the distribution of the matching factors or their relation to the outcome in the source population, one can estimate and study patterns in absolute rates. Throughout, we emphasize distinctions from the more intuitive impacts of cohort matching.

Entities:  

Keywords:  Bias; Case–control studies; Confounding; Matching; Odds ratio

Mesh:

Year:  2017        PMID: 29101596      PMCID: PMC9188848          DOI: 10.1007/s10654-017-0325-0

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   12.434


  39 in total

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Authors:  Tobias Kurth; Alexander M Walker; Robert J Glynn; K Arnold Chan; J Michael Gaziano; Klaus Berger; James M Robins
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2.  Remarks on the method of propensity score.

Authors:  Judea Pearl
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3.  Matched designs and causal diagrams.

Authors:  Mohammad A Mansournia; Miguel A Hernán; Sander Greenland
Journal:  Int J Epidemiol       Date:  2013-06       Impact factor: 7.196

4.  Studying noncollapsibility of the odds ratio with marginal structural and logistic regression models.

Authors:  Menglan Pang; Jay S Kaufman; Robert W Platt
Journal:  Stat Methods Med Res       Date:  2013-10-09       Impact factor: 3.021

5.  Re: "Estimating relative risk functions in case-control studies using a nonparametric logistic regression".

Authors:  S Greenland
Journal:  Am J Epidemiol       Date:  1997-11-15       Impact factor: 4.897

6.  Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists.

Authors:  Mohammad Ali Mansournia; Julian P T Higgins; Jonathan A C Sterne; Miguel A Hernán
Journal:  Epidemiology       Date:  2017-01       Impact factor: 4.822

7.  Matching on a non-risk factor in the design of case-control studies does not always result in an efficiency loss.

Authors:  L A Kalish
Journal:  Am J Epidemiol       Date:  1986-03       Impact factor: 4.897

8.  The interpretation of multiplicative-model parameters as standardized parameters.

Authors:  S Greenland; G Maldonado
Journal:  Stat Med       Date:  1994-05-30       Impact factor: 2.373

9.  The design of case-control studies: the influence of confounding and interaction effects.

Authors:  P G Smith; N E Day
Journal:  Int J Epidemiol       Date:  1984-09       Impact factor: 7.196

10.  The efficiency of matching in case-control studies of risk-factor interactions.

Authors:  D C Thomas; S Greenland
Journal:  J Chronic Dis       Date:  1985
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  34 in total

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Authors:  Albert Hofman
Journal:  Eur J Epidemiol       Date:  2018-12       Impact factor: 8.082

2.  Individually-matched etiologic studies: classical estimators made new again.

Authors:  James A Hanley
Journal:  Eur J Epidemiol       Date:  2018-08-24       Impact factor: 8.082

3.  Toward eradicating misconceptions on matching in etiological studies.

Authors:  Igor Karp
Journal:  Eur J Epidemiol       Date:  2018-03-26       Impact factor: 8.082

4.  Bias in matched case-control studies: DAGs are not enough.

Authors:  Neil Pearce
Journal:  Eur J Epidemiol       Date:  2018-01-25       Impact factor: 8.082

5.  Theory and methodology: essential tools that can become dangerous belief systems.

Authors:  Sander Greenland; Nicholas Patrick Jewell; Mohammad Ali Mansournia
Journal:  Eur J Epidemiol       Date:  2018-05       Impact factor: 8.082

6.  Considerations for outcome-dependent biased sampling in health databases.

Authors:  Sherri Rose
Journal:  Stat Med       Date:  2019-09-30       Impact factor: 2.373

7.  Increased risk of hip fracture mortality associated with intraoperative hypotension in elderly hip fracture patients is related to under resuscitation.

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8.  The association between alopecia areata and anxiety, depression, schizophrenia, and bipolar disorder: a population-based study.

Authors:  Dana Tzur Bitan; Daniella Berzin; Khalaf Kridin; Arnon Cohen
Journal:  Arch Dermatol Res       Date:  2021-06-05       Impact factor: 3.017

9.  Matched Versus Unmatched Analysis of Matched Case-Control Studies.

Authors:  Fei Wan; Graham A Colditz; Siobhan Sutcliffe
Journal:  Am J Epidemiol       Date:  2021-09-01       Impact factor: 4.897

10.  Return-to-Play Times and Player Performance After ACL Reconstruction in Elite UEFA Professional Soccer Players: A Matched-Cohort Analysis From 1999 to 2019.

Authors:  Brian Forsythe; Ophelie Z Lavoie-Gagne; Enrico M Forlenza; Connor C Diaz; Randy Mascarenhas
Journal:  Orthop J Sports Med       Date:  2021-05-28
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