Impact of risk of generalizability biases in adult obesity interventions: A meta‐epidemiological review and meta‐analysis
2022
Beets, Michael W. | von Klinggraeff, Lauren | Burkart, Sarah | Jones, Alexis | Ioannidis, John P. A. | Weaver, R Glenn | Okely, Anthony D. | Lubans, David | van Sluijs, Esther | Jago, Russell | Turner‐McGrievy, Gabrielle | Thrasher, James | Li, Xiaoming
Biases introduced in early‐stage studies can lead to inflated early discoveries. The risk of generalizability biases (RGBs) identifies key features of feasibility studies that, when present, lead to reduced impact in a larger trial. This meta‐study examined the influence of RGBs in adult obesity interventions. Behavioral interventions with a published feasibility study and a larger scale trial of the same intervention (e.g., pairs) were identified. Each pair was coded for the presence of RGBs. Quantitative outcomes were extracted. Multilevel meta‐regression models were used to examine the impact of RGBs on the difference in the effect size (ES, standardized mean difference) from pilot to larger scale trial. A total of 114 pairs, representing 230 studies, were identified. Overall, 75% of the pairs had at least one RGB present. The four most prevalent RGBs were duration (33%), delivery agent (30%), implementation support (23%), and target audience (22%) bias. The largest reductions in the ES were observed in pairs where an RGB was present in the pilot and removed in the larger scale trial (average reduction ES −0.41, range −1.06 to 0.01), compared with pairs without an RGB (average reduction ES −0.15, range −0.18 to −0.14). Eliminating RGBs during early‐stage testing may result in improved evidence.
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