AI 中文总结
针对基于试验的卫生经济学评价中项目级缺失数据的统计挑战，本文提出一种适配复杂特征的贝叶斯纵向插补模型，经真实试验验证了其灵活性与适用性。
AI 中文摘要
基于试验的卫生经济学评价被广泛用于评估医疗干预的成本-效果，为决策提供依据。成本与效果结局通常通过在多个时间点实施的多项目问卷收集，且常存在项目级缺失。原则上，应在项目层面进行插补（即用估计或替代值替换缺失值）以充分利用可用信息，但由于纵向数据结构、项目间依赖关系、异质性缺失模式，以及偏态成本与计数数据的混合等统计挑战，这种做法在实践中很少实施。本文开发了一种贝叶斯纵向模型，用于基于试验的卫生经济学评价中项目级缺失数据的插补，该模型在统一框架内适配上述复杂性。该方法结合了用于纵向依赖的转移模型公式、灵活的分布假设，以及对项目间关系的显式建模，可对不同类型的项目级响应随时间进行连贯建模。受一项真实试验的启发，我们展示了所提方法的灵活性与实际适用性，还讨论了该模型如何扩展至数据可能为非随机缺失的场景。
英文摘要
Trial-based economic evaluations are widely used to assess the cost-effectiveness of healthcare interventions and inform decision-making. Cost and effectiveness outcomes are typically collected using multi-item questionnaires administered at multiple time points, and are often subject to item-level missingness. In principle, imputation (i.e., replacing missing value with estimated or substituted values) should be performed at the item level to fully exploit available information. However, this is rarely implemented in practice due to several statistical challenges, including the longitudinal data structure, cross-item dependence, heterogeneous missingness patterns, and the mixture of skewed cost and count data. In this paper, we develop a Bayesian longitudinal model for imputing item-level missing data in trial-based economic evaluations that accommodates these complexities within a unified framework. The approach combines a transition-model formulation for longitudinal dependence with flexible distributional assumptions and explicit modelling of cross-item relationships, allowing item-level responses of different types to be coherently modelled over time. Motivated by a real-world trial, we demonstrate the flexibility and practical applicability of the proposed approach. We further discuss how the model can be extended to settings where data may be missing not at random.
