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Adjusting for differential proportions of second‐line treatment in cancer clinical trials. Part I: Structural nested models and marginal structural models to test and estimate treatment arm effects
Author(s) -
Yamaguchi Takuhiro,
Ohashi Yasuo
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1816
Subject(s) - marginal structural model , randomization , marginal model , econometrics , randomized controlled trial , causal inference , clinical trial , medicine , statistics , computer science , mathematics , regression analysis , surgery
In randomized trials, post‐randomization variables such as compliance, prescription of alternative treatments and so on are usually ignored to compare treatment arms. Intent‐to‐treat (ITT) analysis is a standard approach but it does not adjust for those variables. However, we may need to evaluate treatment arm effects that have the desired causal interpretation. Previously proposed methods such as time‐dependent Cox model may not properly adjust for post‐randomization variables and may produce biased results. Alternatively, we propose to use two causal models, structural nested models and marginal structural models. The two models appropriately adjust for such variables. We apply these models to adjust for differential proportions of post‐randomization second‐line treatment in cancer clinical trials. With sufficient care to several assumptions, these methods, especially structural nested failure time models with randomized analyses, are useful to take the influence of second‐line treatment into account and to test and estimate the direct treatment arm effect. Copyright © 2004 John Wiley & Sons, Ltd.