Eliciting and using expert opinions about dropout bias in randomized controlled trials
Eliciting and using expert opinions about dropout bias in randomized controlled trials
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DOI：
10.1177/1740774507077849
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发表时间：
2007-01-01
期刊：
影响因子：
2.7
通讯作者：
Schroter, Sara
中科院分区：
文献类型：
--
作者：
White, Ian R.;Carpenter, James;Schroter, Sara
背景 对退出临床试验的分析通常假设缺失的数据是“随机缺失”，即给定个体过去观察到的数据，他们退出的概率并不取决于他们当前的结果。然而，在许多情况下，这种假设是难以置信的，因此评估结论对随机缺失的稳健性是明智的。 目的 开发一种实用、易于理解的方法，使专家对临床试验分析中随机缺失程度的意见能够有意义且准确地得出，并纳入敏感性分析中。 方法 我们引出专家对每个试验组中缺失结果和观察结果之间的平均差异的先前信念。然后，我们使用（i）完整的贝叶斯分析（我们给出 WinBUGS 代码）和（ii）估计治疗效果及其标准误差的简单近似公式，对试验数据中的信息与专家先验的信息进行贝叶斯综合。我们通过重新分析最近一项旨在提高同行评审质量的干预措施试验来说明我们的方法。结果在同行评审试验中，近似公式与完整的贝叶斯分析非常一致，并且两者都显示出比假设随机缺失的分析大得多的标准误差。局限性 严格来说，该方法仅适用于结果呈正态分布的情况。我们没有得出完整的二元先验分布，而是使用了敏感性分析。我们的方法并非旨在纳入有关干预效果本身的先验信念。结论我们提出的方法允许因可能丢失信息的数据缺失而带来更大的不确定性。因此，它可以说是一种真正保守的方法，与“最后观察结转”等方法不同。它对于非统计学家来说是实用且易于理解的。应将其视为未来临床试验设计和分析的一部分。
Background The analysis of clinical trials with dropout usually assumes the missing data are 'missing at random', i.e. given an individual's past observed data, their probability of dropout does not depend on their present outcome. However, in many settings this assumption is implausible, so it is sensible to assess the robustness of conclusions to departures from missing at random.Purpose To develop a practical, accessible, approach that allows expert opinions about the degree of departure from missing at random in the analysis of a clinical trial to be meaningfully and accurately elicited and incorporated in sensitivity analysis.Methods We elicit experts' prior beliefs about the mean difference between missing and observed outcomes in each trial arm. Then we perform a Bayesian synthesis of the information in the trial data with that in the experts' prior, using (i) a full Bayesian analysis for which we give WinBUGS code, and (ii) a simple approximate formula for the estimated treatment effect and its standard error. We illustrate our approach by re-analysing a recent trial of interventions to improve the quality of peer review.Results In the peer review trial, the approximate formula agreed well with the full Bayesian analysis, and both showed substantially larger standard errors than an analysis assuming missing at random. Limitations Strictly, the method is only applicable if the outcome is normally distributed. We did not elicit the full bivariate prior distribution, and instead used a sensitivity analysis. Our approach is not designed to incorporate prior beliefs about the intervention effect itself.Conclusions Our proposed approach allows for the greater uncertainty introduced by missing data that are potentially informatively missing. It can therefore claim to be a truly conservative method, unlike methods such as 'last observation carried forward'. It is practical and accessible to non-statisticians. It should be considered as part of the design and analysis of future clinical trials.