Individual-level effectiveness and healthcare resource use (HRU) data are routinely collected in trial-based economic evaluations. While effectiveness is often expressed in terms of utility scores derived from some health-related quality of life instruments (e.g.~EQ-5D questionnaires), different types of HRU may be included. Costs are usually generated by applying unit prices to HRU data and statistical methods have been traditionally implemented to analyse costs and utilities or after combining them into aggregated variables (e.g. Quality-Adjusted Life Years). When outcome data are not fully observed, e.g. some patients drop out or only provided partial information, the validity of the results may be hindered both in terms of efficiency and bias. Often, partially-complete HRU data are handled using "ad-hoc" methods, implicitly relying on some assumptions (e.g. fill-in a zero) which are hard to justify beside the practical convenience of increasing the completion rate. We present a general Bayesian framework for the modelling of partially-observed HRUs which allows a flexible model specification to accommodate the typical complexities of the data and to quantify the impact of different types of uncertainty on the results. We show the benefits of using our approach using a motivating example and compare the results to those from traditional analyses focussed on the modelling of cost variables after adopting some ad-hoc imputation strategy for HRU data.

翻译：在基于临床试验的经济评价中，个体层面的疗效与医疗资源使用数据被常规收集。疗效通常通过健康相关生活质量测量工具（如EQ-5D问卷）得出的效用评分来表征，而HRU数据可能包含多种资源类型。成本通常通过对HRU数据应用单位价格来生成，传统统计方法多用于分析成本与效用，或将二者合并为综合变量（如质量调整生命年）后进行分析。当结局数据存在部分缺失时（例如患者中途退出或仅提供部分信息），研究结果的效率与无偏性均可能受到影响。当前对部分缺失的HRU数据常采用"临时性"处理方法（例如直接填补零值），这些方法虽能提高数据完整率，但其隐含的假设往往缺乏理论依据。本文提出一个用于建模部分观测HRU数据的通用贝叶斯框架，该框架支持灵活的模型设定以适配数据典型复杂性，并能量化各类不确定性对结果的影响。通过实证案例，我们展示了该方法的优势，并与传统分析策略（即对HRU数据采用临时插补方法后聚焦成本变量建模）的结果进行了对比。