metaanalyseslike

Metaanalyseslike is a term used to describe research activities that resemble meta-analysis in aiming to synthesize results across multiple studies or data sources to estimate an overall effect or pattern, but which may not strictly meet formal meta-analytic criteria. In practice, metaanalyseslike work involves systematic search, data extraction, and quantitative or semi-quantitative synthesis that yields a pooled estimate or an overview of evidence.

Key elements often include preregistered protocols, explicit inclusion criteria, and transparent reporting. When data permit, effect sizes such as risk ratios, odds ratios, mean differences, or standardized mean differences are combined using fixed-effect or random-effects models; when data are too heterogeneous or incompatible, researchers may use narrative synthesis, vote-counting, or qualitative synthesis instead of formal pooling. Assessment of heterogeneity, sensitivity analyses, and checks for publication bias (for example, funnel plots or statistical tests) are common.

Benefits include increased statistical power, clearer direction of evidence, and the ability to explore moderators or study design as sources of variation. Limitations arise from biased study selection, poor-quality primary studies, inconsistent outcome definitions, and ecological fallacies. In many fields, metaanalyseslike work is guided by reporting standards such as PRISMA or MOOSE, and may be preregistered in repositories. The term emphasizes methodological kinship with meta-analysis while acknowledging flexible or pragmatic approaches to evidence synthesis.