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晚期癌症免疫疗法研究设计中的统计挑战。

Statistical Challenges in the Design of Late-Stage Cancer Immunotherapy Studies.

机构信息

Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania.

Department of Global Biometric Sciences, Bristol-Myers Squibb, Princeton, New Jersey. Department of Biostatistics, Columbia University, New York, New York.

出版信息

Cancer Immunol Res. 2015 Dec;3(12):1292-8. doi: 10.1158/2326-6066.CIR-15-0260.

Abstract

The past several years have witnessed a revival of interest in cancer immunology and immunotherapy owing to striking immunologic and clinical responses to immune-directed anticancer therapies and leading to the selection of "Cancer Immunotherapy" as the 2013 Breakthrough of the Year by Science. But statistical challenges exist at all phases of clinical development. In phase III trials of immunotherapies, survival curves have been shown to demonstrate delayed clinical effects, as well as long-term survival. These unique survival kinetics could lead to loss of statistical power and prolongation of study duration. Statistical assumptions that form the foundations for conventional statistical inference in the design and analysis of phase III trials, such as exponential survival and proportional hazards, require careful considerations. In this article, we describe how the unique characteristics of patient response to cancer immunotherapies will impact our strategies on statistical design and analysis in late-stage drug development.

摘要

过去几年,由于免疫导向抗癌疗法产生了显著的免疫和临床反应,癌症免疫学和免疫疗法重新引起了人们的兴趣,并促使“癌症免疫疗法”被《科学》杂志选为 2013 年的年度突破。但是,在临床开发的各个阶段都存在统计学挑战。在免疫疗法的 III 期临床试验中,已经证明生存曲线显示出延迟的临床效果以及长期生存。这些独特的生存动力学可能导致统计效力的丧失和研究持续时间的延长。在 III 期临床试验的设计和分析中构成传统统计推断基础的统计假设,如指数生存和比例风险,需要仔细考虑。在本文中,我们描述了癌症免疫疗法对患者反应的独特特征将如何影响我们在药物后期开发中进行统计设计和分析的策略。

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