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

Pål C Ryalen1, Mats J Stensrud2, Kjetil Røysland2

  • 1Department of Biostatistics, University of Oslo, Domus Medica Gaustad, Sognsvannsveien, 0372, Oslo, Norway. p.c.ryalen@medisin.uio.no.

Lifetime Data Analysis
|February 25, 2019
PubMed
Summary
This summary is machine-generated.

Continuous-time marginal structural models (MSMs) offer a causal analysis alternative for survival data. This study provides methods for consistent estimation of treatment weights and hazard transformations, improving causal inference accuracy.

Keywords:
Additive hazard modelsCausal inference in survival analysisContinuous time marginal structural modelsContinuous time weights

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Marginal structural models (MSMs) are used for causal analysis in longitudinal data.
  • Standard MSMs use discrete time, while continuous-time MSMs are suitable for survival analysis.
  • Treatment weights in MSMs are often assumed known but typically require estimation.

Purpose of the Study:

  • To establish conditions for consistent continuous-time MSM estimation with estimated weights.
  • To demonstrate the utility of additive hazard models for weight estimation.
  • To present a general strategy for transforming hazard estimates for improved causal interpretation.

Main Methods:

  • Developed a sufficient condition for consistent continuous-time MSM estimation.
  • Utilized additive hazard models to estimate treatment weights.
  • Applied a general transformation strategy to weighted cumulative hazard estimates.
  • Introduced R packages 'ahw' and 'transform.hazards' for practical application.

Main Results:

  • Continuous-time MSMs are consistent even with estimated weights under specific conditions.
  • Additive hazard models provide an effective method for estimating weights.
  • Continuous-time weights outperform Inverse Probability of Treatment Weighting (IPTW) for continuous processes.
  • The transformation strategy successfully yields interpretable causal parameters.

Conclusions:

  • Continuous-time MSMs provide a robust framework for causal inference in survival analysis.
  • Accurate estimation of treatment weights is crucial for reliable causal effect estimates.
  • The proposed methods and R packages facilitate practical implementation and enhance causal interpretability.