Moodie Erica E M, Richardson Thomas S
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University.
Scand Stat Theory Appl. 2009 Sep 22;37(1):126-146. doi: 10.1111/j.1467-9469.2009.00661.x.
A dynamic regime provides a sequence of treatments that are tailored to patient-specific characteristics and outcomes. In 2004 James Robins proposed g-estimation using structural nested mean models for making inference about the optimal dynamic regime in a multi-interval trial. The method provides clear advantages over traditional parametric approaches. Robins' g-estimation method always yields consistent estimators, but these can be asymptotically biased under a given structural nested mean model for certain longitudinal distributions of the treatments and covariates, termed exceptional laws. In fact, under the null hypothesis of no treatment effect, every distribution constitutes an exceptional law under structural nested mean models which allow for interaction of current treatment with past treatments or covariates. This paper provides an explanation of exceptional laws and describes a new approach to g-estimation which we call Zeroing Instead of Plugging In (ZIPI). ZIPI provides nearly identical estimators to recursive g-estimators at non-exceptional laws while providing substantial reduction in the bias at an exceptional law when decision rule parameters are not shared across intervals.
动态策略提供了一系列根据患者特定特征和结果量身定制的治疗方法。2004年，詹姆斯·罗宾斯提出了使用结构嵌套均值模型进行g估计，以便在多区间试验中推断最优动态策略。该方法相对于传统参数方法具有明显优势。罗宾斯的g估计方法总能产生一致的估计量，但在给定的结构嵌套均值模型下，对于某些治疗和协变量的纵向分布（称为特殊法则），这些估计量可能会有渐近偏差。事实上，在无治疗效果的原假设下，对于允许当前治疗与过去治疗或协变量相互作用的结构嵌套均值模型，每种分布都构成一种特殊法则。本文对特殊法则进行了解释，并描述了一种新的g估计方法，我们称之为“归零而非代入”（ZIPI）。在非特殊法则下，ZIPI提供的估计量与递归g估计量几乎相同，而当决策规则参数在各区间不共享时，在特殊法则下能大幅减少偏差。
