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Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study

Author

Listed:
  • Xiao Yongling

    (McGill University)

  • Abrahamowicz Michal

    (McGill University & Montreal General Hospital)

  • Moodie Erica E. M.

    (McGill University)

Abstract

Marginal structural models (MSM) provide a powerful tool to control for confounding by a time-dependent covariate without inappropriately adjusting for its role as a variable affected by treatment (Hernán et al., 2000). In this paper, we demonstrate that it is possible to fit a marginal structural Cox model directly, rather than the typical approach of using pooled logistic regression, using the weighted Cox proportional hazards function that has been implemented in standard software. To evaluate the performance of the marginal structural Cox model directly via inverse probability of treatment weighting, we conducted several simulation studies based on two data-generating models: one which replicates the simulations of Young et al. (2009) and an additional, more clinically plausible approach which mimics survival data with time-dependent confounders and time-varying treatment. Using the simulations, we illustrate the limitations of the conventional time-dependent Cox model and the MSM fitted via pooled logistic regression. Furthermore, we propose two novel normalized weights with the goal of reducing the MSM estimators' variability. The performance of the normalized weights is evaluated alongside the usual unstabilized and stabilized weights.

Suggested Citation

  • Xiao Yongling & Abrahamowicz Michal & Moodie Erica E. M., 2010. "Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, March.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:13
    DOI: 10.2202/1557-4679.1208
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    Citations

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    Cited by:

    1. Erica Moodie & D. Stephens, 2011. "Marginal Structural Models: unbiased estimation for longitudinal studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(1), pages 117-119, February.
    2. Mireille E. Schnitzer & Erica E.M. Moodie & Mark J. van der Laan & Robert W. Platt & Marina B. Klein, 2014. "Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 70(1), pages 144-152, March.
    3. Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).

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