论文标题
贝叶斯预后协变量调整
Bayesian prognostic covariate adjustment
论文作者
论文摘要
有关疾病结果的历史数据可以在许多方面纳入临床试验的分析。我们以现有文献为基础，该文献使用预测模型的预后得分来通过协变量调整提高治疗效果估计的效率。在这里，我们走得更远，利用贝叶斯框架将预后协变量调整与从预后模型的预测性能中学到的经验性先验分布相结合。贝叶斯方法在预后协变量调整之间通过严格的I型误差控制在弥漫性时进行插值，而当先验峰值峰值时进行了单臂试验。从理论上讲，该方法可提供统计能力的大幅度增加，同时限制了在合理条件下的I型错误率。我们证明了我们在模拟中的实用性，并分析了过去的阿尔茨海默氏病临床试验。
Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates via covariate adjustment. Here we go further, utilizing a Bayesian framework that combines prognostic covariate adjustment with an empirical prior distribution learned from the predictive performances of the prognostic model on past trials. The Bayesian approach interpolates between prognostic covariate adjustment with strict type I error control when the prior is diffuse, and a single-arm trial when the prior is sharply peaked. This method is shown theoretically to offer a substantial increase in statistical power, while limiting the type I error rate under reasonable conditions. We demonstrate the utility of our method in simulations and with an analysis of a past Alzheimer's disease clinical trial.
