Chapter 11 – Dealing with Missing Outcome Data in Meta-Analysis

Ian R. White, Dimitris Mavridis

Abstract

Missing data can cause bias and uncertainty in single studies. This bias and uncertainty naturally affect meta-analysis of studies that contain incomplete outcome data. Conventional analysis using only individuals with available data is adequate when the meta-analyst can be confident that data are missing at random in every study – that is, that the observed data are representative of all the outcome data for each arm. Such confidence is usually unjustified. Two methods, each based on a plausible assumption about data that are missing, may be used to compensate for missing data. Either the distribution of reasons for missing data may be used to impute the missing values, or the analyst may specify the magnitude and uncertainty of possible departures from the missing at random assumption, and these may be used to correct bias and re-weight the studies. The methods are illustrated in two examples with binary and continuous outcomes.

Corrections

There are currently no corrections for this chapter.

Resources

A relevant paper is an article by Anna Chaimani et al in the Stata Journal – see link below.

Chaimani-et-al.-Allowing-for-informative-missingness-Stata-Journal-2018Download

Practicals

See the files below for a practical exercise in Stata. Note that the metamiss2 command can be installed from within Stata by typing ssc install metamiss2. The R code will follow shortly.

Stata-practical-for-missing-data-in-meta-analysisDownload
practical-do-fileDownload
practical-log-fileDownload
haloperidol dataDownload
mirtazapine dataDownload

Author affiliations

Ian R. White

MRC Clinical Trials Unit at UCL, London, UK

Dimitris Mavridis

Department of Primary Education, University of Ioannina, Ioannina, Greece

How to cite this chapter?

For the printed version of the book

White, I.R. and Mavridis, D. (2022). Chapter 11. Dealing with missing outcome data in meta-analysis. In: Systematic Reviews in Health Research: Meta-analysis in Context (eds M. Egger, J.P.T. Higgins and G. Davey Smith), pp 204-219. Hoboken, NJ : Wiley.

For the electronic version of the book

White, I.R. and Mavridis, D. (2022). Chapter 11. Dealing with missing outcome data in meta-analysis. In: Systematic Reviews in Health Research: Meta-analysis in Context (eds M. Egger, J.P.T. Higgins and G. Davey Smith). https://doi.org/10.1002/9781119099369.ch11