Systematic reviews and meta-analyses are pivotal for evidence-based decision-making but depend on the availability of precise statistical data. Researchers often encounter studies where essential statistics are missing or presented only in graphs, leading to potential data exclusion and selection bias. This study aims to provide specific methodologies for extracting or reconstructing the statistical parameters required for meta-analysis—specifically effect sizes (MD, OR, RR, HR) and their corresponding variance measures (SD, SE, variance)—from incomplete or graphically reported data. We describe calculation and extraction protocols for five specific scenarios encountered in medical literature: (1) continuous data missing standard deviations; (2) categorical data missing standard errors; (3) calculating risk estimates from frequency tables; (4) extracting continuous data presented solely in graphs; and (5) reconstructing hazard ratios from Kaplan-Meier survival curves. Valid meta-analysis requires both an effect size and a measure of variance. When these are not explicitly reported, they can often be derived from other available statistics or digital extraction from figures. While heterogeneity is inherent in meta-analysis, the methodology allows for error adjustment and robust synthesis. Therefore, preventing data loss via these extraction methods is preferable to excluding studies. Maximizing data inclusion enhances the comprehensive value and statistical power of the final analysis.
Shin et al. (Mon,) studied this question.