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    Area of Science:

    • Health Sciences
    • Social Sciences
    • Biostatistics
    • Epidemiology

    Background:

    • Causal inference for longitudinal treatments on mortality is crucial in health and social sciences.
    • Time-varying confounding, where treatment and risk factors influence each other over time, complicates estimation.
    • Marginal Structural Models (MSMs) are established tools for addressing time-varying confounding in longitudinal studies.

    Purpose of the Study:

    • To introduce a novel pooled Targeted Maximum Likelihood Estimator (TMLE) for Marginal Structural Models (MSMs) in the context of longitudinal dynamic treatment regimes.
    • To address limitations of the traditional Inverse Probability Weighted estimation (IPTW) method, particularly its sensitivity to treatment weight misspecification and large weights.
    • To provide a semiparametric efficient and doubly robust alternative for estimating intervention-specific counterfactual hazard functions.

    Main Methods:

    • Development and application of a pooled Targeted Maximum Likelihood Estimator (TMLE) for hazard MSMs.
    • Utilizing the substitution principle inherent in TMLE to potentially mitigate sensitivity to large treatment weights.
    • Comparison of the proposed TMLE with the incumbent IPTW estimator and a non-targeted substitution estimator via simulation studies.

    Main Results:

    • The proposed TMLE demonstrates bias reduction compared to IPTW when treatment probabilities are misspecified.
    • TMLE shows potential for mitigating the sensitivity to large treatment weights often encountered with IPTW.
    • Simulation results indicate favorable performance of the TMLE in estimating hazard functions under dynamic treatment regimes.

    Conclusions:

    • The pooled TMLE offers a robust and efficient method for causal effect estimation in longitudinal studies with time-varying confounding.
    • TMLE provides a valuable alternative to IPTW, particularly in scenarios with complex dynamic treatment strategies and potential misspecification of treatment probabilities.
    • This approach enhances the reliability of estimating intervention-specific counterfactual hazards in observational health and social science research.