Toward Efficient Estimation of Regional Treatment Effects in Multi-Regional Clinical Trials
多区域临床试验中区域处理效应的有效估计AI 解读 ↓
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多区域临床试验中区域处理效应的有效估计
- 研究问题
- 多区域随机试验中，如何利用其他地区的信息提高目标地区处理效应的估计效率，同时避免直接合并异质地区造成偏差？
- 方法与思路
- 作者使用包含地区交互项的工作回归模型，以 adaptive lasso 识别零交互项，从而选择性借用其他地区及基线协变量信息。
- 材料陈述的贡献
- 摘要称工作模型误设时估计量仍一致且渐近正态；若地区间相似性体现为零交互项，可较仅用本地数据提高效率。
- 阅读关注点
- 应核对区域目标效应定义、随机化与渐近条件、惩罚参数选择，以及交互项筛选后的标准误和选择性借用机制。
- 阅读边界
- 来源为 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明筛选和渐近结果的具体条件，效率提升取决于地区相似性。
原文摘要
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.