Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

TL;DR

This paper compares sequential trials and marginal structural models with inverse probability weighting for causal inference in survival analysis using longitudinal observational data, highlighting their assumptions, efficiency, and application to cystic fibrosis data.

Contribution

It demonstrates how the sequential trials approach can estimate the same causal parameters as MSM-IPTW and compares their efficiency and assumptions through simulations and real data.

Findings

Sequential trials can estimate the same marginal risk differences as MSM-IPTW.

Sequential trials tend to have less extreme weights, leading to greater efficiency in most scenarios.

At late follow-up times, MSM-IPTW may outperform sequential trials in certain cases.

Abstract

Longitudinal observational patient data can be used to investigate the causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for controlling for the time-dependent confounding that typically occurs. The most commonly used is inverse probability weighted estimation of marginal structural models (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, in particular in combination with the target trial emulation framework. This approach involves creating a sequence of `trials' from new time origins, restricting to individuals as yet untreated and meeting other eligibility criteria, and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment status at the start of each `trial' (initiator/non-initiator) and this is addressed using inverse probability of censoring weights. The analysis is based on data combined across trials. We show that the sequential trials approach can estimate the parameter of a particular MSM, and compare it to a MSM-IPTW with respect to the estimands being identified, the assumptions needed and how data are used differently. We show how both approaches can estimate the same marginal risk differences. The two approaches are compared using a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at late time points. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.