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Exploratory Analyses for Missing Data in Meta-Analyses and Meta-Regression: A Tutorial.

Jacob M Schauer1, Karina Diaz2, Therese D Pigott3

  • 1Northwestern University, 680 N Lake Shore Dr, Ste 1400, Chicago, IL 60611, USA.

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Summary
This summary is machine-generated.

This tutorial introduces exploratory missingness analysis (EMA) to identify data gaps in meta-analyses. Applying EMA techniques helps researchers understand and address missing data, improving evidence quality.

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Area of Science:

  • Data Science
  • Biostatistics
  • Meta-Analysis

Background:

  • Missing data is a common challenge in meta-analysis, potentially biasing results and limiting evidence synthesis.
  • Understanding the patterns and sources of missingness is crucial for accurate interpretation of meta-analytic findings.

Purpose of the Study:

  • To present methods for exploring missing data in datasets, aiding in the identification of missingness sources and extent.
  • To demonstrate how exploratory missingness analysis (EMA) can clarify gaps in the evidence base within meta-analyses.

Main Methods:

  • Utilized raw data from a meta-analysis of substance abuse interventions to illustrate EMA techniques.
  • Employed numerical summaries and visual displays to analyze missing data patterns and relationships among variables.

Main Results:

  • Demonstrated the examination of missing covariate patterns in meta-analysis data.
  • Revealed complex relationships between missing data, observed variables, and effect sizes in meta-regression.
  • Highlighted specific gaps in the evidence base through a case study analysis.

Conclusions:

  • Recommends that meta-analysts incorporate exploratory missingness analysis (EMA) when dealing with missing data.
  • Suggests EMA can enhance the rigor and transparency of meta-analytic research.