A systematic review identifies a lack of standardization in methods for handling missing variance data

Background and ObjectivesTo describe and critically appraise available methods for handling missing variance data in meta-analysis (MA).MethodsSystematic review. MEDLINE, EMBASE, Web of Science, MathSciNet, Current Index to Statistics, BMJ SearchAll, The Cochrane Library and Cochrance Colloquium proceedings, MA texts and references were searched. Any form of text was included: MA, method chapter, or otherwise. Descriptions of how to implement each method, the theoretic basis and/or ad hoc motivation(s), and the input and output variable(s) were extracted and assessed. Methods may be: true imputations, methods that obviate the need for a standard deviation (SD), or methods that recalculate the SD.ResultsEight classes of methods were identified: algebraic recalculations, approximate algebraic recalculations, imputed study-level SDs, imputed study-level SDs from nonparametric summaries, imputed study-level correlations (e.g., for change-from-baseline SD), imputed MA-level effect sizes, MA-level tests, and no-impute methods.ConclusionThis work aggregates the ideas of many investigators. The abundance of methods suggests a lack of consistency within the systematic review community. Appropriate use of methods is sometimes suspect; consulting a statistician, early in the review process, is recommended. Further work is required to optimize method choice to alleviate any potential for bias and improve accuracy. Improved reporting is also encouraged.