Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis

Dimitris Mavridis, Ian R White, Julian P T Higgins, Andrea Cipriani, Georgia Salanti

Research output: Contribution to journalArticle (Academic Journal)

33 Citations (Scopus)

Abstract

Missing outcome data are commonly encountered in randomized controlled trials and hence may need to be addressed in a meta-analysis of multiple trials. A common and simple approach to deal with missing data is to restrict analysis to individuals for whom the outcome was obtained (complete case analysis). However, estimated treatment effects from complete case analyses are potentially biased if informative missing data are ignored. We develop methods for estimating meta-analytic summary treatment effects for continuous outcomes in the presence of missing data for some of the individuals within the trials. We build on a method previously developed for binary outcomes, which quantifies the degree of departure from a missing at random assumption via the informative missingness odds ratio. Our new model quantifies the degree of departure from missing at random using either an informative missingness difference of means or an informative missingness ratio of means, both of which relate the mean value of the missing outcome data to that of the observed data. We propose estimating the treatment effects, adjusted for informative missingness, and their standard errors by a Taylor series approximation and by a Monte Carlo method. We apply the methodology to examples of both pairwise and network meta-analysis with multi-arm trials. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Original languageEnglish
JournalStatistics in Medicine
DOIs
Publication statusPublished - 13 Nov 2014

Bibliographical note

© 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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