Estimation of Patient Recruitment Using Summary Data Aggregated Across Trials
提出一种利用公开历史试验的汇总数据来估计III期临床试验患者招募模型参数的方法，通过最大似然法估计泊松-伽马模型参数，并在高血压和2型糖尿病试验中验证了准确性。
Accurate modeling of patient recruitment is critical for helping ensure operational efficiency of clinical trials. However, current methods to estimate key model parameters typically require granular center-level data, which can be challenging to obtain. We propose using summary-level data aggregated from publicly available historical trials to help ameliorate these problems. Specifically, we introduce an estimation framework that estimates Poisson-Gamma model parameters of phase III trials by leveraging data from multiple public trials using a maximum likelihood approach. Our framework has desirable theoretical properties in the objective function, which facilitate solution development. Finally, we demonstrate that our approach can accurately estimate model parameters in both simulations and real data case studies of hypertension and type 2 diabetes trials from ClinicalTrials.gov. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0780 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0780 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .