…James R Carpenter, reader in medical and social statistics5 …CarpenterJ, Kenward M.
Brief comments on computational issues with multiple imputation. www.missingdata.org.uk/mi_comp_issues.pdf …

Non-randomised studies of the effects of interventions are critical to many areas of healthcare
evaluation, but their results may be biased. It is therefore important to understand and …

The heterogeneity statistic I 2, interpreted as the percentage of variability due to heterogeneity
between studies rather than sampling error, depends on precision, that is, the size of the …

Meta-analysis plays a key role in evidence synthesis in many research disciplines, not least
the social sciences, medicine and economics. The aim of this book is to equip those …

Missing covariate data commonly occur in epidemiological and clinical research, and are
often dealt with using multiple imputation. Imputation of partially observed covariates is …

Objective To resolve uncertainty as to the risk of Sudden Infant Death Syndrome (SIDS)
associated with sleeping in bed with your baby if neither parent smokes and the baby is breastfed…

Multiple imputation (MI) is now well established as a flexible, general, method for the analysis
of data sets with missing values. Most implementations assume the missing data are `…

Objective: Missing data are ubiquitous in clinical trials, yet recent research suggests many
statisticians and investigators appear uncertain how to handle them. The objective is to set out …

Multiple imputation is now a well‐established technique for analysing data sets where some
units have incomplete observations. Provided that the imputation model is correct, the …

… Donders and Karel GM Moons wrote the first draft of the paper, whichJames R.Carpenter
and Douglas G. Altman critically reviewed. All authors contributed to revisions of the paper. …