Adjusting for differential proportions of second-line treatment in cancer clinical trials. Part I: Structural nested models and marginal structural models to test and estimates treatment arm effects

Takuhiro Yamaguchi, Yasuo Ohashi

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1991-2003
Number of pages13
JournalStatistics in Medicine
Volume23
Issue number13
DOIs
Publication statusPublished - 2004 Jul 15
Externally publishedYes

Keywords

  • Cancer clinical trials
  • Casual inference
  • Direct effect
  • ITT analysis
  • Post-randomized variables
  • Second-line treatment

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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