发表机构
University of California, San Francisco; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco; The Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco; Health Economics & Outcomes Research; University of British Columbia, Vancouver, Canada; University of Bristol, UK; Novo Nordisk Pharma, Madrid, Spain; University College London, UK（旧金山加利福尼亚大学; 旧金山加利福尼亚大学 UCSF 海伦·迪勒家庭综合癌症中心; 旧金山加利福尼亚大学菲利普·李卫生政策研究所; 健康经济学与结果研究; 不列颠哥伦比亚大学; 布里斯托大学; 诺和诺德制药; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨模型化成本效果分析中边际与条件输入的可迁移性问题，提出个体水平模拟延迟边际化以准确估计边际成本效果，并强调记录输入类型与人群的重要性。
AI 中文摘要
目的：人们日益认识到边际估计与条件估计之间的差异，以及不同类型效应测量在不同目标人群中的适用性。当（国际）试验的治疗效果应用于（国家特定）基线风险估计时，这一可迁移性问题在基于模型的成本效果分析（CEA）中备受关注。本文旨在提高人们对在基于模型的CEA中使用不同类型治疗效果和基线风险估计以支持卫生技术评估（HTA）时出现的问题的认识。方法：我们阐明了可压缩性、边际与条件估计以及可迁移性；推导了边际成本效果估计目标所隐含的理想建模方法；并描述了常见建模方法的问题，通过一个虚构的状态转移模型加以说明。结果：一个个体水平模拟，从条件输入预测结果并在目标人群上取平均，针对的是边际成本效果估计目标。队列模型方法过早边际化输入、在平均协变量处评估结果回归模型、或将条件效应与边际基线（或反之）结合，即使使用正确人群的输入也可能错误陈述成本效果结果；来自错误人群的输入则进一步增加误差。结论：最严谨的方法是个体水平模拟，携带条件输入（基线风险、治疗效果、预后效应和效应修饰因子）并延迟边际化。队列方法则过早边际化，依赖汇总输入，不一定针对HTA感兴趣的边际成本效果估计目标。模型开发者应记录每个输入是边际的还是条件的，以及其所属人群。
英文摘要
Objectives: There has been increased appreciation of the differences between marginal and conditional estimates and different types of effect measures regarding their applicability to different target populations. This issue of transportability is of concern in model-based cost-effectiveness analysis (CEA) when treatment effects from (international) trials are applied to (country-specific) baseline risk estimates. The objective of this paper is to create awareness regarding the issues that arise when using different types of treatment effect and baseline risk estimates in a model-based CEA to inform health technology assessment (HTA). Methods: We clarify collapsibility, marginal versus conditional estimation, and transportability; derive the ideal modeling approach implied by a marginal cost-effectiveness estimand; and characterize the issues of common modeling approaches, illustrated with a fictitious state-transition model. Results: An individual-level simulation that predicts outcomes from conditional inputs and averages them over the target population targets the marginal cost-effectiveness estimand. Cohort-model approaches that marginalize inputs early, use conditional inputs at mean covariate values, or combine a conditional effect with a marginal baseline (or vice versa) can misstate cost-effectiveness results even with correct-population inputs; inputs from the wrong population add further error. Conclusions: The most rigorous approach is an individual-level simulation that carries conditional inputs (baseline risk, treatment effect, prognostic effects and effect modifiers) and marginalizes late. Cohort approaches instead marginalize early, relying on aggregated inputs, and do not necessarily target the marginal cost-effectiveness estimand of interest for HTA. Model developers should document, for each input, whether it is marginal or conditional and its population.
