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  5. Are the Risk of Generalizability Biases Generalizable? A Meta-Epidemiological Study

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Preprint
en
2024

Are the Risk of Generalizability Biases Generalizable? A Meta-Epidemiological Study

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0 Files

en
2024
DOI: 10.21203/rs.3.rs-3897976/v1

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John P A Ioannidis
John P A Ioannidis

Stanford University

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Lauren von Klinggraeff
Chris D. Pfledderer
Sarah Burkart
+13 more

Abstract

Abstract Background Preliminary studies (e.g., pilot/feasibility studies) can result in misleading evidence that an intervention is ready to be evaluated in a large-scale trial when it is not. Risk of Generalizability Biases (RGBs, a set of external validity biases) represent study features that influence estimates of effectiveness, often inflating estimates in preliminary studies which are not replicated in larger-scale trials. While RGBs have been empirically established in interventions targeting obesity, the extent to which RGBs generalize to other health areas is unknown. Understanding the relevance of RGBs across health behavior intervention research can inform organized efforts to reduce their prevalence. Purpose The purpose of our study was to examine whether RGBs generalize outside of obesity-related interventions. Methods A systematic review identified health behavior interventions across four behaviors unrelated to obesity that follow a similar intervention development framework of preliminary studies informing larger-scale trials (i.e., tobacco use disorder, alcohol use disorder, interpersonal violence, and behaviors related to increased sexually transmitted infections). To be included, published interventions had to be tested in a preliminary study followed by testing in a larger trial (the two studies thus comprising a study pair). We extracted health-related outcomes and coded the presence/absence of RGBs. We used meta-regression models to estimate the impact of RGBs on the change in standardized mean difference (ΔSMD) between the preliminary study and larger trial. Results We identified sixty-nine study pairs, of which forty-seven were eligible for inclusion in the analysis (k = 156 effects), with RGBs identified for each behavior. For pairs where the RGB was present in the preliminary study but removed in the larger trial the treatment effect decreased by an average of ΔSMD=-0.38 (range − 0.69 to -0.21). This provides evidence of larger drop in effectiveness for studies containing RGBs relative to study pairs with no RGBs present (treatment effect decreased by an average of ΔSMD =-0.24, range − 0.19 to -0.27). Conclusion RGBs may be associated with higher effect estimates across diverse areas of health intervention research. These findings suggest commonalities shared across health behavior intervention fields may facilitate introduction of RGBs within preliminary studies, rather than RGBs being isolated to a single health behavior field.

How to cite this publication

Lauren von Klinggraeff, Chris D. Pfledderer, Sarah Burkart, Kaitlyn Ramey, Michal T. Smith, Alexander C. McLain, Bridget Armstrong, R. Glenn Weaver, Anthony D. Okely, David R. Lubans, John P A Ioannidis, Russell Jago, Gabrielle Turner‐McGrievy, James F. Thrasher, Xiaoming Li, Michael W. Beets (2024). Are the Risk of Generalizability Biases Generalizable? A Meta-Epidemiological Study. , DOI: https://doi.org/10.21203/rs.3.rs-3897976/v1.

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Publication Details

Type

Preprint

Year

2024

Authors

16

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.21203/rs.3.rs-3897976/v1

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