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A double robust approach to causal effects in case-control studies.

Sherri Rose, Mark van der Laan

    American Journal of Epidemiology
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    This study evaluates methods for estimating additive interaction in case-control studies. It highlights limitations of inverse probability weighting and introduces a superior targeted maximum likelihood estimator for interaction analysis.

    Keywords:
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    Area of Science:

    • Epidemiology
    • Biostatistics

    Background:

    • Inverse probability weighting (IPW) is a method used in case-control studies to estimate additive interaction effects, such as relative excess risk due to interaction.
    • The previously described IPW method has known disadvantages that can impact the reliability of the estimated interaction effect.

    Purpose of the Study:

    • To critically evaluate the inverse probability weighting method for estimating additive interaction in case-control studies.
    • To introduce and discuss the advantages of the case-control-weighted targeted maximum likelihood estimator (cc-tmle) as an alternative to IPW.
    • To highlight the improved statistical properties of cc-tmle compared to existing IPW estimators.

    Main Methods:

    • Review and critique of the inverse probability weighting method for additive interaction in case-control studies.
    • Introduction and discussion of the case-control-weighted targeted maximum likelihood estimator (cc-tmle).
    • Comparison of the properties of cc-tmle with previously described inverse-probability-weighted estimators.

    Main Results:

    • The study reinforces the well-known disadvantages associated with inverse probability weighting methods.
    • The case-control-weighted targeted maximum likelihood estimator (cc-tmle) is presented as a double robust estimator with improved properties.
    • cc-tmle offers a viable alternative for estimating various epidemiological parameters, including risk difference, relative risk, and odds ratio.

    Conclusions:

    • Inverse probability weighting methods have significant limitations for estimating additive interaction in case-control studies.
    • The case-control-weighted targeted maximum likelihood estimator (cc-tmle) provides a more robust and reliable approach.
    • cc-tmle is a versatile tool for estimating multiple epidemiological parameters of interest in case-control settings.