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

Author

Listed:
  • Rose Sherri

    (University of California, Berkeley)

  • van der Laan Mark J.

    (University of California, Berkeley)

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.

Suggested Citation

  • Rose Sherri & van der Laan Mark J., 2009. "Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-26, January.
  • Handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:1
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    Citations

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    Cited by:

    1. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
    2. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    3. Flynn, Rachel M. & Lissy, Rachel & Alicea, Stacey & Tazartes, Lisa & McKay, Mary M., 2016. "Professional development for teachers plus coaching related to school-wide suspensions for a large urban school system," Children and Youth Services Review, Elsevier, vol. 62(C), pages 29-39.
    4. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
    5. Sherri Rose & Julie Shi & Thomas G. McGuire & Sharon-Lise T. Normand, 0. "Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-18.
    6. repec:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-015-9135-7 is not listed on IDEAS
    7. Yu-Jen Cheng & Mei-Cheng Wang, 2012. "Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 707-716, September.
    8. van der Laan Mark J. & Gruber Susan, 2012. "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-41, May.
    9. 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.
    10. Christopher Vahl & Qing Kang, 2015. "Analysis of an outcome-dependent enriched sample: hypothesis tests," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 387-409, September.

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