Charting the Scientific Landscape of Indirect Estimation Models in Doping Prevalence Research: A Bibliometric Analysis with Narrative Appraisal

Abstract

Interpreting doping prevalence estimates generated through indirect estimation models (IEMs) remains challenging for sport policy and governance due to the wide variation in reported rates and methodological complexity. In this study, we combined a critical appraisal of the methodological and epistemic developments of IEM applications to doping prevalence with a bibliometric analysis of publication trends, citation patterns, and collaboration networks, using a convergent parallel mixed-methods design. Across 52 records published between 2002 and 2026, this study maps the scientific landscape of IEM-based doping prevalence research. Findings show that IEM-based prevalence research is methodologically sophisticated yet institutionally dispersed and largely Eurocentric, reflecting a field still consolidating its standards and disciplinary identity. Over time, the focus has shifted from reporting prevalence rates to methodological critique and re-analysis of existing datasets. Reported prevalence estimates, ranging from 0 to 57.1%, are highly sensitive to modelling assumptions about athlete behaviour in complex survey environments. While this trend strengthens rigour, it also complicates evidence synthesis for policy actors and risks undermining trust in IEM-based estimates if poorly communicated. Anti-doping organisations and researchers should treat IEM-derived prevalence as bounded indicators rather than definitive rates and integrate prevalence evidence with contextual data for transparent policy and public communication.