Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation
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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 study matched services that implemented the tool to similar control services to ensure effects were not confounded by other factors.
“To ensure that the effects observed were not confounded by other factors, we matched the services that implemented the AI-enabled self-referral tool to other similar services as commonly done in similar clinical and public health research studies [Rose and Van der Laan, 2009].”
From Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot (2024) · cites this paper as Rose and Van der Laan, 2009
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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)
- 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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