Imputation methods for missing outcome data in meta-analysis of clinical trials
Imputation methods for missing outcome data in meta-analysis of clinical trials
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DOI：
10.1177/1740774508091600
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发表时间：
2008-01-01
期刊：
影响因子：
2.7
通讯作者：
Wood, Angela M.
中科院分区：
文献类型：
--
作者：
Higgins, Julian P. T.;White, Ian R.;Wood, Angela M.
背景：随机试验结果数据的缺失导致了更大的不确定性和估计实验治疗效果的可能偏差。意向治疗分析应考虑所有随机参与者，即使他们有缺失的观察结果。目的回顾和发展具有二元结果的临床试验荟萃分析中缺失结果数据的归算方法。方法我们回顾了一些常见的策略，如简单地推测阳性或阴性结果，并开发了一种涉及“信息缺失比值比”（IMORs）的通用方法。我们描述了meta分析中权重研究的几种选择，并说明了使用氟哌啶醇治疗精神分裂症试验的meta分析方法。结果IMORs描述了失踪参与者的未知风险与观察参与者的已知风险之间的关系。它们可以在不同的治疗组和不同的试验中有所不同。将IMORS和其他方法应用于氟哌啶醇试验表明，对于缺失数据的不同假设，总体结论是稳健的。该方法基于每个干预组每个试验的汇总数据（观察到的阳性结果数、观察到的阴性结果数和缺失结果数）。这就限制了分析的选择，对于个体参与者的数据将有更大的灵活性。结论：我们建议利用可获得的缺失原因来确定合适的IMORs。我们还建议采用一种策略进行敏感性分析，其中IMORs在合理范围内变化。
Background Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.Purpose To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes.Methods We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving 'informative missingness odds ratios' (IMORs'). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia.Results IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORS and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data.Limitations The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data.Conclusions We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges.