通过倾向分数加权的分部分力量先验增强对照组以改善临床试验的运作特性
Improving operating characteristics of clinical trials by augmenting control arm using propensity score-weighted borrowing-by-parts power prior
AI总结：
本文提出一种结合倾向分数加权和灵活贝叶斯方法的PSW-BPP，用于改善临床试验中外部数据借用的效率和稳健性。
摘要：
借用外部数据可以提高估计效率，但当人群在协变量分布或结局变异上存在差异时可能会引入偏差。需要在两个数据集之间保持适当平衡以证明借用的合理性。我们提出了一种倾向分数加权的分部分力量先验（PSW-BPP），该方法通过倾向分数加权进行因果协变量调整，并结合灵活的贝叶斯借用方法，以统一框架解决这些挑战。所提出的方法首先使用倾向分数加权使外部数据的协变量分布与当前研究一致，从而针对共同的估计目标并减少由于人口异质性导致的混杂。然后将加权的外部似然纳入贝叶斯模型中，通过分部分力量先验进行借用，允许对似然的均值和方差成分分别设置不同的功率参数，实现差异化的和校准的信息借用。此外，我们采用最小可能性指数（mPI）的概念来计算功率参数。这种分开的借用方法相比传统功率先验方法，对先验数据冲突具有更强的鲁棒性。我们通过广泛的模拟研究和一个实际数据示例研究了PSW-BPP的运作特性。模拟研究显示，PSW-BPP在无借用和固定借用的情况下提供了更高效和稳定的估计，特别是在中等协变量不平衡和结局异质性的情况下。所提出的框架为贝叶斯推断中使用外部数据进行观察性研究和混合研究设计提供了系统且可扩展的方法论贡献。
英文摘要：
Borrowing external data can improve estimation efficiency but may introduce bias when populations differ in covariate distributions or outcome variability. A proper balance needs to be maintained between the two datasets to justify the borrowing. We propose a propensity score weighting borrowing-by-parts power prior (PSW-BPP) that integrates causal covariate adjustment through propensity score weighting with a flexible Bayesian borrowing approach to address these challenges in a unified framework. The proposed approach first applies propensity score weighting to align the covariate distribution of the external data with that of the current study, thereby targeting a common estimand and reducing confounding due to population heterogeneity. The weighted external likelihood is then incorporated into a Bayesian model through a borrowing-by-parts power prior, which allows distinct power parameters for the mean and variance components of the likelihood, enabling differential and calibrated information borrowing. Additionally, we adopt the idea of the minimal plausibility index (mPI) to calculate the power parameters. This separate borrowing provides greater robustness to prior-data conflict compared with traditional power prior methods that impose a single borrowing parameter. We study the operating characteristics of PSW-BPP through extensive simulation and a real data example. Simulation studies demonstrate that PSW-BPP yields more efficient and stable estimation than no borrowing and fixed borrowing, particularly under moderate covariate imbalance and outcome heterogeneity. The proposed framework offers a principled and extensible methodological contribution for Bayesian inference with external data in observational and hybrid study designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
