We develop and demonstrate methods to perform sensitivity analyses to assess sensitivity to plausible departures from missing at random in incomplete repeated binary outcome data. We use multiple imputation in the not at random fully conditional specification framework, which includes one or more sensitivity parameters (SPs) for each incomplete variable. The use of an online elicitation questionnaire is demonstrated to obtain expert opinion on the SPs, and highest prior density regions are used alongside opinion pooling methods to display credible regions for SPs. We demonstrate that substantive conclusions can be far more sensitive to departures from the missing at random assumption (MAR) when control and intervention nonresponders depart from MAR differently, and show that the correlation of arm specific SPs in expert opinion is particularly important. We illustrate these methods on the iQuit in Practice smoking cessation trial, which compared the impact of a tailored text messaging system versus standard care on smoking cessation. We show that conclusions about the effect of intervention on smoking cessation outcomes at 8 week and 6 months are broadly insensitive to departures from MAR, with conclusions significantly affected only when the differences in behavior between the nonresponders in the two trial arms is larger than expert opinion judges to be realistic.

中文翻译：

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对不完整重复二进制数据进行启发、插补和敏感性分析的通用方法。

我们开发并演示了执行敏感性分析的方法，以评估对不完整重复二元结果数据中随机缺失的合理偏离的敏感性。我们在非随机完全条件规范框架中使用多重插补，其中包括每个不完整变量的一个或多个敏感度参数 (SP)。演示了使用在线启发式问卷来获得对 SP 的专家意见，并且最高先验密度区域与意见汇集方法一起使用以显示 SP 的可信区域。我们证明，当控制和干预无反应者以不同方式偏离 MAR 时，实质性结论对偏离随机缺失假设 (MAR) 更为敏感，并表明专家意见中手臂特定 SP 的相关性尤为重要。我们在 iQuit in Practice 戒烟试验中说明了这些方法，该试验比较了量身定制的短信系统与标准护理对戒烟的影响。我们表明，关于干预对第 8 周和第 6 个月戒烟结果的影响的结论对偏离 MAR 不敏感，只有当两个试验组中无反应者之间的行为差​​异大于专家意见时，结论才会受到显着影响裁判要实事求是。