Trial‐based economic evaluations are typically performed on cross‐sectional variables, derived from the responses for only the completers in the study, using methods that ignore the complexities of utility and cost data (e.g. skewness and spikes). We present an alternative and more efficient Bayesian parametric approach to handle missing longitudinal outcomes in economic evaluations, while accounting for the complexities of the data. We specify a flexible parametric model for the observed data and partially identify the distribution of the missing data with partial identifying restrictions and sensitivity parameters. We explore alternative non‐ignorable missingness scenarios through different priors for the sensitivity parameters, calibrated on the observed data. Our approach is motivated by, and applied to, data from a trial assessing the cost‐effectiveness of a new treatment for intellectual disability and challenging behaviour.

中文翻译：

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在基于试验的健康经济评估中处理缺失纵向结果数据的贝叶斯参数方法

基于试验的经济评估通常是对横截面变量进行的，这些变量源自研究中仅完成者的反应，使用的方法忽略了效用和成本数据的复杂性（例如偏度和峰值）。我们提出了一种替代且更有效的贝叶斯参数方法来处理经济评估中缺失的纵向结果，同时考虑到数据的复杂性。我们为观察到的数据指定了一个灵活的参数模型，并通过部分识别限制和敏感性参数部分识别缺失数据的分布。我们通过灵敏度参数的不同先验探索替代的不可忽略的缺失场景，根据观察到的数据进行校准。我们的方法的动机并应用于，