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在具有事件发生时间终点的临床试验中对历史对照数据进行贝叶斯利用。

Bayesian leveraging of historical control data for a clinical trial with time-to-event endpoint.

作者信息

Roychoudhury Satrajit, Neuenschwander Beat

机构信息

Pfizer Inc, New York, New York.

Novartis Pharma AG, Basel, Switzerland.

出版信息

Stat Med. 2020 Mar 30;39(7):984-995. doi: 10.1002/sim.8456. Epub 2020 Jan 27.

Abstract

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials.

摘要

最近的《21世纪治愈法案》推动创新,以加速21世纪疗法的发现、开发和推广。该法案包括贝叶斯统计的更广泛应用以及临床专业知识证据的使用。后者的一个例子是使用试验外部(或历史)数据,这有望实现更高效或更符合伦理的试验设计。我们提出一种贝叶斯荟萃分析方法,以利用历史数据来分析事件发生时间终点,这在肿瘤学和心血管疾病中很常见。该方法基于用于分段指数数据的稳健分层模型。它允许试验间存在不同程度的异质性,并能够利用个体数据和汇总数据。一项卵巢癌试验和一项非小细胞肺癌试验说明了利用历史数据进行事件发生时间试验分析和设计的方法学及实际应用方面。

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