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Handling missing values in cost effectiveness analyses that use data from cluster randomized trials
Author(s) -
DíazOrdaz K.,
Kenward Michael G.,
Grieve Richard
Publication year - 2014
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12016
Subject(s) - missing data , bivariate analysis , imputation (statistics) , data mining , computer science , randomized controlled trial , multilevel model , cluster (spacecraft) , statistics , econometrics , medicine , machine learning , mathematics , surgery , programming language
Summary 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.