A Systematic Review of Methods for Handling Missing Variance Data in Meta-Analyses of Interventions in Type 2 Diabetes Mellitus.

Abstract

Meta-analysis is of critical importance to decision makers to assess the comparative efficacy and safety of interventions and is integral to health technology assessment. A major problem for the meta-analysis of continuous outcomes is that associated variance data are not consistently reported in trial publications. The omission of studies from a meta-analysis due to incomplete reporting may introduce bias. The objectives of this study are to summarise and describe the methods used for handling missing variance data in meta-analyses in populations with type 2 diabetes mellitus (T2DM).Electronic databases, Embase, MEDLINE, and the Cochrane Library (accessed June 2015), were systematically searched to identify meta-analyses of interventions in patients with T2DM. Eligible studies included those which analysed the change in HbA1c from baseline.Sixty-seven publications reporting on meta-analyses of change in HbA1c from baseline in T2DM were identified. Approaches for dealing with missing variance data were reported in 41% of publications and included algebraic calculation, trial-level imputation, and no imputation.Meta-analysis publications typically fail to report standardised approaches for dealing with missing variance data. While no particular imputation method is favoured, authors are discouraged from using a no-imputation approach. Instead, authors are encouraged to explore different approaches using sensitivity analyses and to improve the quality of reporting by documenting the methods used to deal with missing variance data.