Electronic Journal of Statistics

Causal inference in longitudinal studies with history-restricted marginal structural models

Romain Neugebauer, Mark J. van der Laan, Marshall M. Joffe, and Ira B. Tager

Full-text: Open access

Abstract

A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be better suited for public health research or at least more practicable than MSMs (6, 2). HRMSMs allow investigators to analyze the causal effect of a treatment on an outcome based on a fixed, shorter and user-specified history of exposure compared to MSMs. By default, the latter represent the treatment causal effect of interest based on a treatment history defined by the treatments assigned between the study’s start and outcome collection. We lay out in this article the formal statistical framework behind HRMSMs. Beyond allowing a more flexible causal analysis, HRMSMs improve computational tractability and mitigate statistical power concerns when designing longitudinal studies. We also develop three consistent estimators of HRMSM parameters under sufficient model assumptions: the Inverse Probability of Treatment Weighted (IPTW), G-computation and Double Robust (DR) estimators. In addition, we show that the assumptions commonly adopted for identification and consistent estimation of MSM parameters (existence of counterfactuals, consistency, time-ordering and sequential randomization assumptions) also lead to identification and consistent estimation of HRMSM parameters.

Article information

Source
Electron. J. Statist., Volume 1 (2007), 119-154.

Dates
First available in Project Euclid: 9 May 2007

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1178738531

Digital Object Identifier
doi:10.1214/07-EJS050

Mathematical Reviews number (MathSciNet)
MR2312147

Zentralblatt MATH identifier
1320.62217

Keywords
causal inference counterfactual marginal structural model longitudinal study IPTW G-computation Double Robust

Citation

Neugebauer, Romain; van der Laan, Mark J.; Joffe, Marshall M.; Tager, Ira B. Causal inference in longitudinal studies with history-restricted marginal structural models. Electron. J. Statist. 1 (2007), 119--154. doi:10.1214/07-EJS050. https://projecteuclid.org/euclid.ejs/1178738531


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