Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials
Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials
复制标题
DOI：
10.1177/009286150804200402
复制
发表时间：
2008-01-01
影响因子：
--
通讯作者：
Mancuso, James P.
中科院分区：
文献类型：
--
作者：
Mallinckrodt, Craig H.;Lane, Peter W.;Mancuso, James P.
本立场文件总结了旨在支持药品监管批准的纵向临床试验分析的相关理论和当前实践，并回顾了已发表的关于处理缺失数据方法的研究。这是PhRMA提高后期临床研究效率的举措之一，并提供跨行业团队的建议。当目标是估计和检验给定时间点的治疗差异时，我们特别关注使用线性模型分析的连续反应测量。传统上，此类试验的主要分析通过使用末次或基线观察值结转法（LOCF，BOCF）进行简单插补，然后在选定时间点进行（协）方差分析来处理缺失数据。然而，一般的统计和科学界已经远离这些简单的方法，而倾向于基于多变量模型（例如，混合效应类型）对所有时间点的数据进行联合分析。其中一种较新的方法，基于似然的混合效应模型重复测量（MMRM）方法，在临床试验文献中受到了相当大的关注。我们讨论了监管机构提出的具体问题，就MMRM和审查已发表的证据比较LOCF和MMRM的有效性，偏倚，权力和I型错误。我们的主要结论是，混合模型方法是更有效和可靠的主要分析方法，并应首选固有的偏见和统计无效的简单插补方法。我们还总结了处理缺失数据的其他方法，这些方法可用作敏感性分析，用于评估非随机缺失数据的潜在影响。
This position paper summarizes relevant theory and current practice regarding the analysis of longitudinal clinical trials intended to support regulatory approval of medicinal products, and it reviews published research regarding methods for handling missing data. It is one strand of the PhRMA initiative to improve efficiency of late-stage clinical research and gives recommendations from a cross-industry team. We concentrate specifically on continuous response measures analyzed using a linear model, when the goal is to estimate and test treatment differences at a given time point. Traditionally, the primary analysis of such trials handled missing data by simple imputation using the last, or baseline, observation carried forward method (LOCF, BOCF) followed by analysis of (co)variance at the chosen time point. However, the general statistical and scientific community has moved away from these simple methods in favor of joint analysis of data from all time points based on a multivariate model (eg, of a mixed-effects type). One such newer method, a likelihood-based mixed-effects model repeated measures (MMRM) approach, has received considerable attention in the clinical trials literature. We discuss specific concerns raised by regulatory agencies with regard to MMRM and review published evidence comparing LOCF and MMRM in terms of validity, bias, power, and type I error. Our main conclusion is that the mixed model approach is more efficient and reliable as a method of primary analysis, and should be preferred to the inherently biased and statistically invalid simple imputation approaches. We also summarize other methods of handling missing data that ore useful as sensitivity analyses for assessing the potential effect of data missing not at random.