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Sample size considerations in clinical trials when comparing two interventions using multiple co-primary binary relative risk contrasts

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摘要

It is observed that in clinical trials, in addition to the primary end points the use of co-primary endpoints play an effective role to determine if a new intervention is superior to the control intervention. In this connection, it is discussed about the need for developing new approaches to the design and analysis of clinical trials with co-primary endpoints. It is noted that though there are a number of methodologies that have addressed the use of multiple co-primary continuous endpoints no appropriate methodology is available for multiple binary endpoints. Therefore, in this article, it has been attempted to study the power and sample size determination for clinical trials with multiple correlated binary endpoints, when relative risks are evaluated as co-primary. In this regard a scenario is considered where the objective is to evaluate evidence for superiority of a test intervention compared with a control intervention, for all of the relative risks. The normal approximation method for sample size determination that incorporates the correlations among the endpoints into the calculations is discussed. The practical utility of the normal approximation method via Monte Carlo simulation is also evaluated. A simple but conservative procedure for sample size calculation is considered by treating the endpoints as if they are not correlated. The extension of the proposed methodology to a group-sequential setting is given.

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  • 来源
    《Quality Control and Applied Statistics》 |2017年第2期|27-29|共3页
  • 作者

    YukiAndo; Toshimitsu Hamasaki; Scott R. EvansKoko AsakuraTomoyuki SugimotoTakashi SozuYuko Ohno;

  • 作者单位

    Biostatistics Group, Center for Product Evaluation Pharmaceuticals and Medical Devices Agency, Japan;

    Biostatistics and Data Management, National Cerebral and Cardiovascular Center, Japan;

    Department of Biostatistics and the Center for Biostatistics in AIDS Research, Harvard School of Public Health, Boston, МA 02115Department of Mathematical Sciences Graduate School of Science and Technology Hirosaki University, Hirosaki,JapanDepartment of Management Science, Faculty of Engineering, Tokyo University of Science, JapanDepartment of Mathematical Health Science, Osaka University Graduate School of Medicine, Suita, Osaka, 5650871, Japan;

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  • 原文格式 PDF
  • 正文语种 英语
  • 中图分类 概率论、数理统计的应用;
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