AI 中文总结
本研究针对多区域临床试验，提出一种采用自适应LASSO识别无效交互项以选择性借用跨区域信息的稳健方法，可高效一致地估计区域治疗效应，效率优于本地估计且对模型误设具有稳健性。
AI 中文摘要
多区域临床试验（MRCT）是一项在通用方案下同时于多个区域开展的单一临床试验，可用于支持向多个监管机构的平行申报。对于某一区域的监管机构而言，相较于基于MRCT所有纳入区域的总体治疗效应，专门针对其自身区域定义的治疗效应更具相关性。区域治疗效应可通过目标区域的本地数据进行一致估计，但该方法通常效率较低，因为它排除了其他区域的数据且忽略了区域间可能存在的相似性。另一方面，简单地合并跨区域数据需要较强的假设，若所需假设不满足则可能引入偏倚。在此，我们提出一种简单且稳健的方法，用于估计双臂随机MRCT中的区域治疗效应。该方法使用工作回归模型纳入基线协变量信息及其他区域的数据以提升效率；模型通过交互项解释结局对治疗及协变量的依赖关系可能在区域间存在差异，以此校正测量协变量后残留的区域差异。我们采用自适应LASSO识别无效交互项，从而实现对其他区域信息的选择性借用。所得区域治疗效应估计量即使在工作模型设定错误时仍具有一致性和渐近正态性，且当区域间以无效交互项形式存在相似性时，其效率优于本地估计。
英文摘要
A multi-regional clinical trial (MRCT) is a single clinical trial conducted in multiple regions simultaneously under a common protocol, which may be used to support parallel submissions to multiple regulatory authorities. For a regional regulatory authority, treatment effects defined specifically for its own region are more relevant to consider than overall treatment effects based on all regions included in an MRCT. A regional treatment effect can be estimated consistently using local data from the region of interest; however, this approach is generally inefficient as it excludes data from other regions and ignores possible similarities between regions. On the other hand, simply pooling data across regions requires strong assumptions and may introduce bias when the required assumptions are not met. Here, we propose a simple and robust approach to estimating a regional treatment effect in a two-arm randomized MRCT. The proposed approach uses a working regression model to incorporate information from baseline covariates as well as data from other regions for improved efficiency. The model accounts for residual regional differences (after adjusting for measured covariates) using interaction terms that describe how the dependence of outcome on treatment and covariates may vary across regions. The adaptive lasso is used to identify null interactions and thus achieve selective borrowing of information from other regions. The resulting regional treatment effect estimator is consistent and asymptotically normal even when the working model is misspecified, and able to improve efficiency over local estimation when there are similarities between regions in the form of null interactions.
