Meta-analysis with missing study-level sample variance data.

ABSTRACT: We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing-completely-at-random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression

SUBMITTER: Chowdhry AK 

PROVIDER: S-EPMC4931964 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature