Cited by 293Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

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Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation

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Rose, Sherri, van der Laan, Mark J.·2009·The International Journal of Biostatistics

DOI 10.2202/1557-4679.1127PMC PMC2827892

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Abstract

Matched case-control study designs are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case-control studies is a gain in efficiency. Methods for analyzing matched case-control studies have focused on utilizing conditional logistic regression models that provide conditional and not causal estimates of the odds ratio. This article investigates the use of case-control weighted targeted maximum likelihood estimation to obtain marginal causal effects in matched case-control study designs. We compare the use of case-control weighted targeted maximum likelihood estimation in matched and unmatched designs in an effort to explore which design yields the most information about the marginal causal effect. The procedures require knowledge of certain prevalence probabilities and were previously described by van der Laan (2008). In many practical situations where a causal effect is the parameter of interest, researchers may be better served using an unmatched design.

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The International Journal of Biostatistics, Vol. 5 [2009], Iss. 1, Art. 1 DOI: 10.2202/1557-4679.1127
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The International Journal of Biostatistics, Vol. 5 [2009], Iss. 1, Art. 1 DOI: 10.2202/1557-4679.1127

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Authors: Rose, S., & van der Laan, M. J.
Affiliations: University of California, Berkeley (US)
University of California, Berkeley
ORCID: Rose, Sherri
Venue: The International Journal of Biostatistics
Publisher: Walter de Gruyter GmbH
Published: 2009-01-06
Volume: 5
Issue: 1
Pages: Article 1-Article 1
ISSN: 1557-4679 (ISSN-L)
DOI: 10.2202/1557-4679.1127
PMC: PMC2827892
OpenAlex: W2059121696
Cited by: 293
References: 40
Citations by year: 2026: 3 · 2025: 16 · 2024: 27 · 2023: 20 · 2022: 24 · 2021: 27 · 2020: 26 · 2019: 25 · 2018: 25 · 2017: 24 · 2016: 16 · 2015: 15 · 2014: 14 · 2013: 8 · 2012: 9
FWCI: 4.95
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: The International Journal of Biostatistics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Collection of Biostatistics Research Archive (COBRA) (submittedVersion)
Funders: National Institutes of Health (R01 A1074345-01)
U.S. Public Health Service (R01 A1074345-01)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: matched case-control studies, causal effect estimation, targeted maximum likelihood estimation, conditional logistic regression, confounding
MeSH terms: Humans, Research Design, Models, Statistical, Causality, Likelihood Functions, Case-Control Studies

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Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation

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