Bayesian Benefit-Risk Assessment with Dependent Outcomes via Latent Factor Models
利用潜因子模型对相关结局进行贝叶斯获益—风险评估
Approving and assessing new drugs is complex because multiple criteria must be considered simultaneously. A common approach is benefit-risk analysis, often conducted within a Bayesian framework to account for uncertainty and combine data with expert judgement, typically through multi-criteria decision analysis (MCDA) scores. This requires models that accommodate mixed and potentially correlated outcomes; latent factor models provide a natural framework. We develop a coherent…