一种用于随机试验中对偏离随机缺失假设进行敏感性分析的平均得分方法。
A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials.
作者信息
White Ian R, Carpenter James, Horton Nicholas J
机构信息
MRC Biostatistics Unit, Cambridge, UK.
MRC Clinical Trials Unit at UCL, London, UK.
出版信息
Stat Sin. 2018 Oct;28(4):1985-2003. doi: 10.5705/ss.202016.0308.
Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single outcome analysed under a generalised linear model. One or more sensitivity parameters, specified by the user, measure the degree of departure from missing at random in a pattern mixture model. Advantages of our method are that its sensitivity parameters are relatively easy to interpret and so can be elicited from subject matter experts; it is fast and non-stochastic; and its point estimate, standard error and confidence interval agree perfectly with standard methods when particular values of the sensitivity parameters make those standard methods appropriate. We illustrate the method using data from a mental health trial.
大多数对存在不完整结局的随机试验的分析都做出了无法检验的假设，因此应进行敏感性分析。然而，敏感性分析方法并未得到广泛应用。我们提出一种平均得分方法，用于探究在随机试验中偏离随机缺失或关于不完整结局数据的其他假设时的全局敏感性。我们假设在广义线性模型下分析单个结局。由用户指定的一个或多个敏感性参数，在模式混合模型中衡量偏离随机缺失的程度。我们方法的优点在于其敏感性参数相对易于解释，因此可以从主题专家那里获取；它快速且非随机；当敏感性参数的特定值使那些标准方法适用时，其点估计、标准误差和置信区间与标准方法完全一致。我们使用一项心理健康试验的数据来说明该方法。
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