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倾向评分整合生存分析方法:利用单臂研究中的外部证据。

Propensity score-integrated approach to survival analysis: leveraging external evidence in single-arm studies.

机构信息

Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Baltimore, Maryland, USA.

Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.

出版信息

J Biopharm Stat. 2022 May 4;32(3):400-413. doi: 10.1080/10543406.2022.2080701. Epub 2022 Jun 8.

Abstract

External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.

摘要

外部数据,也称为正在计划的传统临床研究之外的数据,包括但不限于真实世界数据(RWD)和过去或其他国家进行的临床研究中收集的数据。外部数据有时可用于在适当情况下增强单臂前瞻性设计的研究。在这种应用中,当临床结果为二分类或连续时,可以使用最近开发的倾向评分综合方法,包括 PSPP 和 PSCL,用于研究设计和数据分析。在本文中,提出了一种用于类似情况的倾向评分综合 Kaplan-Meier(PSKM)方法,但感兴趣的结果是事件发生时间。倾向评分方法用于选择与当前研究在基线协变量方面相似的外部受试者,并根据来自两个数据源的选择受试者将其分层到更同质的层中。基于具有外部数据的加权的该层中的所有受试者获得特定于层的 PSKM 估计量,然后将这些估计量组合以获得总体 PSKM 估计量。进行了一项模拟研究以评估 PSKM 方法的性能,并提供了一个说明性示例来说明如何实施所提出的方法。

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