Inference for non-regular parameters in optimal dynamic treatment regimes

被引：113

作者：

Chakraborty, Bibhas ^{[1
]}

Murphy, Susan ^{[1
]}

Strecher, Victor ^{[2
]}

机构：

[1] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

[2] Univ Michigan, Ctr Hlth Commun Res, Ann Arbor, MI 48109 USA

来源：

STATISTICAL METHODS IN MEDICAL RESEARCH | 2010年 / 19卷 / 03期

基金：

美国国家卫生研究院;

关键词：

2-STAGE RANDOMIZATION DESIGNS; SURVIVAL DISTRIBUTIONS; TREATMENT POLICIES; BOOTSTRAP; STRATEGIES; TRIALS; SUBJECT;

D O I：

10.1177/0962280209105013

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

A dynamic treatment regime is a set of decision rules, one per stage, each taking a patient's treatment and covariate history as input, and outputting a recommended treatment. In the estimation of the optimal dynamic treatment regime from longitudinal data, the treatment effect parameters at any stage prior to the last can be non-regular under certain distributions of the data. This results in biased estimates and invalid confidence intervals for the treatment effect parameters. In this article, we discuss both the problem of non-regularity, and available estimation methods. We provide an extensive simulation study to compare the estimators in terms of their ability to lead to valid confidence intervals under a variety of non-regular scenarios. Analysis of a data set from a smoking cessation trial is provided as an illustration.

引用

页码：317 / 343

页数：27

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