期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
卷 177, 期 2, 页码 457-474出版社
WILEY
DOI: 10.1111/rssa.12016
关键词
Bivariate models; Clustered continuous data; Cost effectiveness; Missing data; Multiple imputation
资金
- National Institute for Health Research Research Methods Fellowship
- UK Medical Research Council
- MRC [G0802321, G106/1173, MR/L011964/1] Funding Source: UKRI
- Medical Research Council [G106/1173, MR/L011964/1, G0802321] Funding Source: researchfish
- National Institute for Health Research [PDA/03/07/026] Funding Source: researchfish
- National Institutes of Health Research (NIHR) [PDA/03/07/026] Funding Source: National Institutes of Health Research (NIHR)
Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据