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The additive hazard estimator is consistent for continuous-time marginal structural models

Author

Listed:
  • Pål C. Ryalen

    (University of Oslo)

  • Mats J. Stensrud

    (University of Oslo)

  • Kjetil Røysland

    (University of Oslo)

Abstract

Marginal structural models (MSMs) allow for causal analysis of longitudinal data. The standard MSM is based on discrete time models, but the continuous-time MSM is a conceptually appealing alternative for survival analysis. In applied analyses, it is often assumed that the theoretical treatment weights are known, but these weights are usually unknown and must be estimated from the data. Here we provide a sufficient condition for continuous-time MSM to be consistent even when the weights are estimated, and we show how additive hazard models can be used to estimate such weights. Our results suggest that continuous-time weights perform better than IPTW when the underlying process is continuous. Furthermore, we may wish to transform effect estimates of hazards to other scales that are easier to interpret causally. We show that a general transformation strategy can be used on weighted cumulative hazard estimates to obtain a range of other parameters in survival analysis, and explain how this strategy can be applied on data using our R packages ahw and transform.hazards.

Suggested Citation

  • Pål C. Ryalen & Mats J. Stensrud & Kjetil Røysland, 2019. "The additive hazard estimator is consistent for continuous-time marginal structural models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 611-638, October.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:4:d:10.1007_s10985-019-09468-y
    DOI: 10.1007/s10985-019-09468-y
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    References listed on IDEAS

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    1. Joffe, Marshall M. & Ten Have, Thomas R. & Feldman, Harold I. & Kimmel, Stephen E., 2004. "Model Selection, Confounder Control, and Marginal Structural Models: Review and New Applications," The American Statistician, American Statistical Association, vol. 58, pages 272-279, November.
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