White Ian R, Carpenter James, Evans Stephen, Schroter Sara
MRC Biostatistics Unit, Cambridge UK.
Clin Trials. 2007;4(2):125-39. doi: 10.1177/1740774507077849.
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.
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.
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.
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.
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.
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.
对存在失访情况的临床试验进行分析时，通常假定缺失数据是“随机缺失”的，即给定个体过去的观测数据，其失访概率不取决于其当前的结果。然而，在许多情况下，这一假设是不合理的，因此评估结论对于偏离随机缺失假设的稳健性是明智的。
开发一种实用、易懂的方法，以便在临床试验分析中能够有意义且准确地引出关于偏离随机缺失程度的专家意见，并将其纳入敏感性分析。
我们引出专家对每个试验组中缺失结果与观测结果之间平均差异的先验信念。然后，我们使用（i）给出WinBUGS代码的全贝叶斯分析，以及（ii）估计治疗效果及其标准误的简单近似公式，将试验数据中的信息与专家先验信息进行贝叶斯综合。我们通过重新分析最近一项关于改善同行评审质量的干预试验来说明我们的方法。
在同行评审试验中，近似公式与全贝叶斯分析结果吻合良好，并且两者显示的标准误都比假设随机缺失的分析结果大得多。
严格来说，该方法仅在结果呈正态分布时适用。我们没有引出完整的双变量先验分布，而是使用了敏感性分析。我们的方法并非旨在纳入关于干预效果本身的先验信念。
我们提出的方法考虑到了潜在信息性缺失数据所带来的更大不确定性。因此，与“末次观测结转”等方法不同，它可以说是一种真正保守的方法。它对非统计学家来说实用且易懂。在未来临床试验的设计和分析中应考虑采用该方法。
