A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment
A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment
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DOI：
10.1080/19466315.2019.1689847
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发表时间：
2019-12-12
影响因子：
1.8
通讯作者：
Josiassen, Mette Krog
中科院分区：
文献类型：
--
作者：
Larsen, Klaus Groes;Josiassen, Mette Krog
人用药品注册技术要求国际协调会议工作组的ICH E9（R1）增补草案开放用于监管目的的研究数据分析中的主分层（PS），前提是相关估计量合理。受所谓的依从者平均因果效应的启发，并在此框架内开展工作，我们提出了一种新的估计量-基于依从倾向的外推-估计活性药物治疗A相对于对照药物B（活性药物或安慰剂）的治疗效果，如果接受治疗A治疗，则在患者依从的PS中。协变量的数量和预测PS成员的能力的方法的敏感性进行了调查的基础上，安慰剂对照研究的数据，精神分裂症的布雷哌唑。估计的性能进行了比较与另一种估计，也是基于主分层。一个模拟研究支持，建议的估计有一个可以忽略不计的偏差，即使在一个小的样本量，除非当协变量预测遵守是非常弱的。毫不奇怪，随着更强的依从性预测因素的增加，估计的精确度大幅提高。分析表明，用于比较的估计一般是有偏的，并明确推导出的偏差在一个简单的情况下，一个二元预测的遵守。虽然所提出的方法在技术上很容易实现，但在实践中选择用于建模合规性的预测因子是一个关键问题。
The draft ICH E9 (R1) addendum by the International Conference on Harmonisation working group opens for the use of a principal stratum (PS) in the analysis of study data for regulatory purpose, if a relevant estimand can be justified. Inspired by the so-called complier average causal effect and work within this framework, we propose a new estimator-Extrapolation based on propensity to comply-that estimates the treatment effect of an active treatment A relative to a comparator B (active or placebo), in the PS of patients who would comply, if they were treated with treatment A. Sensitivity of the approach to the number of covariates and their ability to predict PS membership is investigated based on data from a placebo-controlled study of brexpiprazole in schizophrenia. The performance of the estimator is compared with another estimator that is also based on principal stratification. A simulation study supports that the proposed estimator has a negligible bias even with a small sample size, except when the covariate predicting compliance is very weak. Not surprisingly, precision of the estimate increases substantially with stronger predictors of compliance. It is shown analytically that the estimator used for comparison is biased in general, and the bias is explicitly derived in a simple case with a binary predictor of compliance. While the proposed methodology is technically easy to implement, choosing predictors for modeling compliance is a key issue in practice.