Multiple Imputation Using Gamma Meta-Regression for Missing Sample Variance Data in Meta-Analysis

• Activity Number: 395
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Amit Kumar Chowdhry*+ and Michael P. McDermott
• Companies: University of Rochester Medical Center and University of Rochester Medical Center
• Keywords: meta-analysis ; missing data ; multiple imputation ; meta-regression ; mean imputation ; complete case analysis

Abstract

Consider a study-level meta-analysis of multiple studies with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the group difference in means. A common complication in such an analysis is missing sample variances for some studies. A frequently-used approach is to singly impute the weighted mean of the observed variances (mean imputation). Another approach employed 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 (MCAR), and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression to impute the missing sample variances. Our method takes advantage of study-level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and power to detect a specified group difference.