Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data
提出一种基于似不相关回归方程的直接回归方法，用于临床试验中成本效果数据的协变量调整和亚组分析，可提高效率且不要求成本和效果的变量集相同。
The current interest in undertaking cost-effectiveness analyses alongside clinical trials has lead to the increasing availability of patient-level data on both the costs and effectiveness of intervention. In a recent paper, we show how cost-effectiveness analysis can be undertaken in a regression framework. In the current paper we develop a direct regression approach to cost-effectiveness analysis by proposing the use of a system of seemingly unrelated regression equations to provide a more general method for prognostic factor adjustment with emphasis on sub-group analysis. This more general method can be used in either an incremental cost-effectiveness or an incremental net-benefit approach, and does not require that the set of independent variables for costs and effectiveness be the same. Furthermore, the method can exhibit efficiency gains over unrelated ordinary least squares regression.