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Semiparametric causal inference in matched cohort studies

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  • E. H. Kennedy
  • A. Sjölander
  • D. S. Small

Abstract

Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimators, which ignore the matching and exposure-based sampling. This is not the case for general average effects. We also show that matched cohort studies are often more efficient than random sampling for estimating effects on the treated, and derive the optimal number of matches for a given set of matching variables. We illustrate our results via simulation and in a matched cohort study of the effect of hysterectomy on the risk of cardiovascular disease.

Suggested Citation

  • E. H. Kennedy & A. Sjölander & D. S. Small, 2015. "Semiparametric causal inference in matched cohort studies," Biometrika, Biometrika Trust, vol. 102(3), pages 739-746.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:3:p:739-746.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv025
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    References listed on IDEAS

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    1. van der Laan Mark J. & Petersen Maya & Zheng Wenjing, 2013. "Estimating the Effect of a Community-Based Intervention with Two Communities," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 83-106, June.
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    Cited by:

    1. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    2. Amanda Coston & Edward H. Kennedy, 2022. "The role of the geometric mean in case-control studies," Papers 2207.09016, arXiv.org.
    3. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Issa J. Dahabreh & Sarah E. Robertson & Lucia C. Petito & Miguel A. Hernán & Jon A. Steingrimsson, 2023. "Efficient and robust methods for causally interpretable meta‐analysis: Transporting inferences from multiple randomized trials to a target population," Biometrics, The International Biometric Society, vol. 79(2), pages 1057-1072, June.

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