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

Lifetime Data Analysis
Pål Christie RyalenKjetil Røysland

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.

References

Aug 24, 2000·Epidemiology·J M RobinsB Brumback
Aug 7, 2008·American Journal of Epidemiology·Stephen R Cole, Miguel A Hernán
Dec 17, 2009·Epidemiology·Miguel A Hernán
Jul 25, 2012·Statistics in Medicine·W G Havercroft, V Didelez
Jun 24, 2015·Lifetime Data Analysis·Odd O AalenKjetil Røysland
Mar 1, 2017·Epidemiology·Mats Julius StensrudOdd O Aalen
Aug 21, 2018·Biostatistics·Pål Christie RyalenKjetil Røysland
Jun 22, 2019·Biometrics·Mats J StensrudPål C Ryalen

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Citations

Jun 22, 2019·Biometrics·Mats J StensrudPål C Ryalen
May 14, 2021·Biometrical Journal. Biometrische Zeitschrift·Ruth H KeoghStijn Vansteelandt

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