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Allowing for uncertainty due to missing data in meta‐analysis—Part 2: Hierarchical models by Ian R. White; Nicky J. Welton; Angela M. Wood; A. E. Ades; Julian P. T. Higgins is a Mathematics article available to read on EtoBox.

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## Abstract We propose a hierarchical model for the analysis of data from several randomized trials where some outcomes are missing. The degree of departure from a missing‐at‐random assumption in each arm of each trial is expressed by an informative missing odds ratio (IMOR). We require a realistic prior for the IMORs, including an assessment of the prior correlation between IMORs in different arms and in different trials. The model is fitted by Monte Carlo Markov Chain techniques. By applying the method in three different data sets, we show that it is possible to appropriately capture the extra uncertainty due to missing data, and we discuss in what circumstances it is possible to learn about the IMOR. Copyright © 2007 John Wiley & Sons, Ltd.

Who reads Allowing for uncertainty due to missing data in meta‐analysis—Part 2: Hierarchical models?

Author

Ian R. White; Nicky J. Welton; Angela M. Wood; A. E. Ades; Julian P. T. Higgins

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2007

Language

EN

Field

Mathematics (Physical Sciences)