Seuc Armando H, Shah Iqbal H, Ali Moazzam, Diaz-Olavarrieta Claudia, Temmerman Marleen
Department of Reproductive Health and Research, World Health Organization, 1211, Geneva 27, Switzerland.
Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA.
Trials. 2015 Nov 7;16:510. doi: 10.1186/s13063-015-1035-0.
The assessment of treatment success in clinical trials when multiple (repeated) doses (courses) are involved is quite common, for example, in the case of infertility treatment with assisted reproductive technology (ART), and medical abortion using misoprostol alone or in combination with mifepristone. Under these or similar circumstances, most researchers assess success using binomial proportions after a certain number of consecutive doses, and some have used survival analysis. In this paper we discuss the main problems in using binomial proportions to summarize (the overall) efficacy after two or more consecutive doses of the relevant treatment, particularly for the case of misoprostol in medical abortion studies. We later discuss why the survival analysis is best suited under these circumstances, and illustrate this by using simulated data.
The formulas required for the binomial proportion and survival analysis (without and with competing risks) approaches are summarized and analytically compared. Additionally, numerical results are computed and compared between the two approaches, for several theoretical scenarios.
The main conceptual limitations of the binomial proportion approach are identified and discussed, caused mainly by the presence of censoring and competing risks, and it is demonstrated how survival analysis can solve these problems. In general, the binomial proportion approach tends to underestimate the "real" success rate, and tends to overestimate the corresponding standard error.
Depending on the rates of censored observations or competing events between repeated doses of the treatment, the bias of the binomial proportion approach as compared to the survival analysis approaches varies; however, the use of the binomial approach is unjustified as the survival analysis options are well known and available in multiple statistical packages. Our conclusions also apply to other situations where success is estimated after multiple (repeated) doses (courses) of the treatment.
在涉及多个（重复）剂量（疗程）的临床试验中评估治疗成功率是很常见的，例如，在辅助生殖技术（ART）治疗不孕症的情况下，以及单独使用米索前列醇或与米非司酮联合使用进行药物流产的情况。在这些或类似情况下，大多数研究人员在一定数量的连续剂量后使用二项比例来评估成功率，一些人则使用生存分析。在本文中，我们讨论了使用二项比例来总结连续两次或更多次相关治疗剂量后的（总体）疗效时的主要问题，特别是在药物流产研究中米索前列醇的情况。我们随后讨论了为什么生存分析在这些情况下最适用，并通过模拟数据进行说明。
总结了二项比例和生存分析（无竞争风险和有竞争风险）方法所需的公式，并进行了分析比较。此外，针对几种理论情况，计算并比较了两种方法的数值结果。
确定并讨论了二项比例方法的主要概念局限性，主要是由删失和竞争风险的存在导致的，并展示了生存分析如何解决这些问题。一般来说，二项比例方法往往会低估“实际”成功率，并往往高估相应的标准误差。
根据治疗重复剂量之间的删失观察率或竞争事件发生率，与生存分析方法相比，二项比例方法的偏差会有所不同；然而，由于生存分析选项在多个统计软件包中是众所周知且可用的，使用二项方法是不合理的。我们的结论也适用于在多次（重复）剂量（疗程）治疗后评估成功率的其他情况。
