Sullivan Thomas R, Yelland Lisa N, Lee Katherine J, Ryan Philip, Salter Amy B
1 School of Public Health, The University of Adelaide, Adelaide, SA, Australia.
2 South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Clin Trials. 2017 Aug;14(4):387-395. doi: 10.1177/1740774517703319. Epub 2017 Apr 6.
BACKGROUND/AIMS: After completion of a randomised controlled trial, an extended follow-up period may be initiated to learn about longer term impacts of the intervention. Since extended follow-up studies often involve additional eligibility restrictions and consent processes for participation, and a longer duration of follow-up entails a greater risk of participant attrition, missing data can be a considerable threat in this setting. As a potential source of bias, it is critical that missing data are appropriately handled in the statistical analysis, yet little is known about the treatment of missing data in extended follow-up studies. The aims of this review were to summarise the extent of missing data in extended follow-up studies and the use of statistical approaches to address this potentially serious problem.
We performed a systematic literature search in PubMed to identify extended follow-up studies published from January to June 2015. Studies were eligible for inclusion if the original randomised controlled trial results were also published and if the main objective of extended follow-up was to compare the original randomised groups. We recorded information on the extent of missing data and the approach used to treat missing data in the statistical analysis of the primary outcome of the extended follow-up study.
Of the 81 studies included in the review, 36 (44%) reported additional eligibility restrictions and 24 (30%) consent processes for entry into extended follow-up. Data were collected at a median of 7 years after randomisation. Excluding 28 studies with a time to event primary outcome, 51/53 studies (96%) reported missing data on the primary outcome. The median percentage of randomised participants with complete data on the primary outcome was just 66% in these studies. The most common statistical approach to address missing data was complete case analysis (51% of studies), while likelihood-based analyses were also well represented (25%). Sensitivity analyses around the missing data mechanism were rarely performed (25% of studies), and when they were, they often involved unrealistic assumptions about the mechanism.
Despite missing data being a serious problem in extended follow-up studies, statistical approaches to addressing missing data were often inadequate. We recommend researchers clearly specify all sources of missing data in follow-up studies and use statistical methods that are valid under a plausible assumption about the missing data mechanism. Sensitivity analyses should also be undertaken to assess the robustness of findings to assumptions about the missing data mechanism.
背景/目的：在一项随机对照试验完成后，可能会启动延长随访期，以了解干预措施的长期影响。由于延长随访研究通常涉及额外的入选限制和参与的同意程序，并且更长的随访期会带来更大的参与者流失风险，因此在这种情况下，缺失数据可能是一个相当大的威胁。作为偏差的一个潜在来源，在统计分析中妥善处理缺失数据至关重要，但对于延长随访研究中缺失数据的处理知之甚少。本综述的目的是总结延长随访研究中缺失数据的程度以及用于解决这一潜在严重问题的统计方法的使用情况。
我们在PubMed中进行了系统的文献检索，以识别2015年1月至6月发表的延长随访研究。如果原始随机对照试验结果也已发表，并且延长随访的主要目的是比较原始随机分组，则这些研究有资格纳入。我们记录了关于缺失数据程度的信息以及在延长随访研究主要结局的统计分析中用于处理缺失数据的方法。
在纳入综述的81项研究中，36项（44%）报告了进入延长随访的额外入选限制，24项（30%）报告了同意程序。随机分组后中位7年收集数据。排除28项以事件发生时间为主要结局的研究后，51/53项研究（96%）报告了主要结局的缺失数据。在这些研究中，主要结局具有完整数据的随机参与者的中位百分比仅为66%。处理缺失数据最常用的统计方法是完全病例分析（51%的研究），基于似然性的分析也占相当比例（25%）。围绕缺失数据机制的敏感性分析很少进行（25%的研究），而且即便进行了，往往涉及对机制的不切实际假设。
尽管缺失数据在延长随访研究中是一个严重问题，但处理缺失数据的统计方法往往并不充分。我们建议研究人员在随访研究中明确指定所有缺失数据的来源，并使用在关于缺失数据机制的合理假设下有效的统计方法。还应进行敏感性分析，以评估研究结果对关于缺失数据机制假设的稳健性。
