Charting the scientific landscape of indirect estimation models in doping prevalence research: a narrative review with bibliometric analysis

Abstract

Interpreting doping prevalence estimates generated through indirect estimation models (IEM) remains challenging for sport policy and governance due to wide variation in reported rates and methodological complexity. Building on Sagoe et al. (2024), we combined a critical narrative review of methodological and epistemic developments with a bibliometric analysis of publication trends, citation patterns, and collaboration networks, using a convergent parallel mixed‑methods design. Across 52 records published between 2002-2026, 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 reanalysis of existing datasets Reported prevalence estimates, ranging from 0 to 57.1%, are highly sensitive to modelling assumptions about athlete behavior in complex survey environments. While this trend strengthens rigor, it also complicates evidence synthesis for policy actors and risks undermining trust in IEM‑based estimates if poorly communicated. Anti‑doping organizations 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.

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ORCID for Peter Van Der Heijden: orcid.org/0000-0002-3345-096X

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