A1061
Title: Simulating data from marginal structural models for a survival time outcome
Authors: Shaun Seaman - University of Cambridge (United Kingdom) [presenting]
Ruth Keogh - London School of Hygiene and Tropical Medicine (United Kingdom)
Abstract: Marginal structural models (MSMs) are often used to estimate the causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, e.g., inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. A method is proposed to overcome these restrictions. The MSM can be, for example, a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. The use of the proposed simulation algorithm is illustrated by carrying out a brief simulation study. The coverage of confidence intervals calculated in two different ways is compared for causal effect estimates obtained by fitting an MSM via IPTW.
