Modified Robust Meta-Analytic-Predictive Priors for Incorporating Historical Controls in Clinical Trials

IF 1.5 4区医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics in Biopharmaceutical Research Pub Date : 2023-09-15 DOI:10.1080/19466315.2023.2241405

Qiang Zhao, Haijun Ma

引用次数: 0

Abstract

Incorporating historical information in clinical trials has been of much interest recently because of its potential to reduce the size and cost of clinical trials. Data-conflict is one of the biggest challenges in incorporating historical information. In order to address the conflict between historical data and current data, several methods have been proposed including the robust meta-analytic-predictive (rMAP) prior method. In this paper, we propose to modify the rMAP prior method by using an empirical Bayes approach to estimate the weights for the two components of the rMAP prior. Via numerical calculations, we show that this modification to the rMAP method improves its performance regarding multiple key metrics.

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在临床试验中纳入历史对照的改良稳健meta分析预测先验

在临床试验中纳入历史信息最近引起了很大的兴趣，因为它有可能减少临床试验的规模和成本。数据冲突是合并历史信息的最大挑战之一。为了解决历史数据和当前数据之间的冲突，已经提出了几种方法，包括鲁棒元分析预测(rMAP)先验方法。在本文中，我们提出通过使用经验贝叶斯方法来估计rMAP先验的两个分量的权重来修改rMAP先验方法。通过数值计算，我们表明对rMAP方法的这种修改提高了其在多个关键指标方面的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics in Biopharmaceutical Research MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

3.90

自引率

16.70%

发文量

期刊介绍： Statistics in Biopharmaceutical Research ( SBR), publishes articles that focus on the needs of researchers and applied statisticians in biopharmaceutical industries; academic biostatisticians from schools of medicine, veterinary medicine, public health, and pharmacy; statisticians and quantitative analysts working in regulatory agencies (e.g., U.S. Food and Drug Administration and its counterpart in other countries); statisticians with an interest in adopting methodology presented in this journal to their own fields; and nonstatisticians with an interest in applying statistical methods to biopharmaceutical problems. Statistics in Biopharmaceutical Research accepts papers that discuss appropriate statistical methodology and information regarding the use of statistics in all phases of research, development, and practice in the pharmaceutical, biopharmaceutical, device, and diagnostics industries. Articles should focus on the development of novel statistical methods, novel applications of current methods, or the innovative application of statistical principles that can be used by statistical practitioners in these disciplines. Areas of application may include statistical methods for drug discovery, including papers that address issues of multiplicity, sequential trials, adaptive designs, etc.; preclinical and clinical studies; genomics and proteomics; bioassay; biomarkers and surrogate markers; models and analyses of drug history, including pharmacoeconomics, product life cycle, detection of adverse events in clinical studies, and postmarketing risk assessment; regulatory guidelines, including issues of standardization of terminology (e.g., CDISC), tolerance and specification limits related to pharmaceutical practice, and novel methods of drug approval; and detection of adverse events in clinical and toxicological studies. Tutorial articles also are welcome. Articles should include demonstrable evidence of the usefulness of this methodology (presumably by means of an application). The Editorial Board of SBR intends to ensure that the journal continually provides important, useful, and timely information. To accomplish this, the board strives to attract outstanding articles by seeing that each submission receives a careful, thorough, and prompt review. Authors can choose to publish gold open access in this journal.