Improving clinical decision making through Bayesian causal inference with irregularly observed longitudinal data

通过不规则观察的纵向数据进行贝叶斯因果推理来改善临床决策

• 批准号: 365453
• 负责人: Liu Kuan
• 金额: $ 7.65万
• 依托单位: Hospital for Sick Children (Toronto)
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-10-01 至 2019-10-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=365453&lang=en
• 关键词: Bayesian Causal Inference; Biostatistics Methodology; Complex Registry And Administrative Databases; Intervention Safety And Efficacy Studies; Longitudinal Data; Marginal Structural Models; Multiple Outputation Procedure; Observational Studies

There is a wealth of observational data available through large administrative databases, with the potential to enable the assessment of rare outcomes or long-term effects that might not have been observable in clinical trials owing to their limited size

通过大型管理数据库可以获得大量的观察数据，这些数据有可能评估由于规模有限而在临床试验中可能无法观察到的罕见结局或长期效应