1. Per-Protocol analyses produced larger treatment effect sizes than intention to treat: a meta-epidemiological study.
- Author
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Mostazir M, Taylor G, Henley WE, Watkins ER, and Taylor RS
- Subjects
- Biomedical Research statistics & numerical data, Humans, Periodicals as Topic statistics & numerical data, Clinical Protocols, Epidemiologic Studies, Intention to Treat Analysis statistics & numerical data, Meta-Analysis as Topic, Randomized Controlled Trials as Topic statistics & numerical data, Research Design statistics & numerical data, Research Design trends
- Abstract
Objective: To undertake meta-analysis and compare treatment effects estimated by the intention-to-treat (ITT) method and per-protocol (PP) method in randomized controlled trials (RCTs). PP excludes trial participants who are non-adherent to trial protocol in terms of eligibility, interventions, or outcome assessment., Study Design and Setting: Five high impact journals were searched for all RCTs published between July 2017 to June 2019. Primary outcome was a pooled estimate that quantified the difference between the treatment effects estimated by the two methods. Results are presented as ratio of odds ratios (ROR). Meta-regression was used to explore the association between level of trial protocol non-adherence and treatment effect. Sensitivity analyses compared results with varying within-study correlations and across various study characteristics., Results: Random-effects meta-analysis (N = 156) showed that PP estimates were on average 2% greater compared to the ITT estimates (ROR: 1.02, 95% CI: 1.00-1.04, P = 0.03). The divergence further increased with higher degree of protocol non-adherence. Sensitivity analyses reassured consistent results with various within-study correlations and across various study characteristics., Conclusion: There was evidence of larger treatment effect with PP compared to ITT analysis. PP analysis should not be used to assess the impact of protocol non-adherence in RCTs. Instead, in addition to ITT, investigators should consider randomization based casual method such as Complier Average Causal Effect (CACE)., (Copyright © 2021. Published by Elsevier Inc.)
- Published
- 2021
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