OBJECTIVES: In this tutorial, we examine methods for exploring missingness in a dataset in ways that can help identify the sources and extent of missingness, as well as clarify gaps in evidence. METHODS: Using raw data from a meta-analysis of substance abuse interventions, we demonstrate the use of exploratory missingness analysis (EMA) including techniques for numerical summaries and visual displays of missing data. RESULTS: These techniques examine the patterns of missing covariates in meta-analysis data and the relationships among variables with missing data and observed variables including the effect size. The case study shows complex relationships among missingness and other potential covariates in meta-regression, highlighting gaps in the evidence base. CONCLUSION: Meta-analysts could often benefit by employing some form of EMA as they encounter missing data.
Abstract: OBJECTIVES: In this tutorial, we examine methods for exploring missingness in a dataset in ways that can help identify the sources and extent of missingness, as well as clarify gaps in evidence.
METHODS: Using raw data from a meta-analysis of substance abuse interventions, we demonstrate the use of exploratory missingness analysis (EMA) including techniques for numerical summaries and visual displays of missing data.
RESULTS: These techniques examine the patterns of missing covariates in meta-analysis data and the relationships among variables with missing data and observed variables including the effect size. The case study shows complex relationships among missingness and other potential covariates in meta-regression, highlighting gaps in the evidence base.
CONCLUSION: Meta-analysts could often benefit by employing some form of EMA as they encounter missing data.