Rabe Brooke A, Day Simon, Fiero Mallorie H, Bell Melanie L
Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.
Clinical Trials Consulting & Training Limited, UK.
Pharm Stat. 2018 Sep;17(5):477-488. doi: 10.1002/pst.1867. Epub 2018 May 25.
Non-inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence).
Our primary aim was to review the extent of and methods for handling missing data (model-based methods, single imputation, multiple imputation, complete case), the analysis sets used (Intention-To-Treat, Per-Protocol, or both), and whether sensitivity analyses were used to explore departures from assumptions about the missing data.
We conducted a systematic review of NI and equivalence trials published between May 2015 and April 2016 by searching the PubMed database. Articles were reviewed primarily by 2 reviewers, with 6 articles reviewed by both reviewers to establish consensus.
Of 109 selected articles, 93% reported some missing data in the primary outcome. Among those, 50% reported complete case analysis, and 28% reported single imputation approaches for handling missing data. Only 32% reported conducting analyses of both intention-to-treat and per-protocol populations. Only 11% conducted any sensitivity analyses to test assumptions with respect to missing data.
Missing data are common in NI and equivalence trials, and they are often handled by methods which may bias estimates and lead to incorrect conclusions.
非劣效性（NI）和等效性临床试验旨在检验一种新治疗方法在治疗效果上是否不劣于或等同于现有的标准治疗方法。临床试验中的缺失数据已被证明会降低统计效力，并可能使效应大小的估计产生偏差；然而，在NI和等效性试验中，缺失数据还会带来其他问题。例如，它们可能会降低对治疗组之间差异的敏感性，并偏向于NI（或等效性）的备择假设。
我们的主要目的是回顾缺失数据的程度和处理方法（基于模型的方法、单一填补、多重填补、完整病例分析）、所使用的分析集（意向性分析集、符合方案分析集或两者皆用），以及是否使用敏感性分析来探讨与缺失数据假设的偏离情况。
我们通过检索PubMed数据库，对2015年5月至2016年4月发表的NI和等效性试验进行了系统评价。文章主要由两名审稿人进行评审，其中6篇文章由两名审稿人共同评审以达成共识。
在109篇入选文章中，93%的文章报告了主要结局中存在一些缺失数据。其中，50%的文章报告了完整病例分析，28%的文章报告了处理缺失数据的单一填补方法。只有32%的文章报告了对意向性分析集和符合方案分析集均进行分析。只有11%的文章进行了任何敏感性分析以检验关于缺失数据的假设。
缺失数据在NI和等效性试验中很常见，并且它们通常采用可能使估计产生偏差并导致错误结论的方法来处理。
