Adherence adjustment in the Coronary Drug Project: A call for better per-protocol effect estimates in randomized trials
Open accessAbstract
Background: In many randomized controlled trials, patients and doctors are more interested in the per-protocol effect than in the intention-to-treat effect. However, valid estimation of the per-protocol effect generally requires adjustment for prognostic factors associated with adherence. These adherence adjustments have been strongly questioned in the clinical trials community, especially after 1980 when the Coronary Drug Project team found that adherers to placebo had lower 5-year mortality than non-adherers to placebo. Methods: We replicated the original Coronary Drug Project findings from 1980 and re-analyzed the Coronary Drug Project data using technical and conceptual developments that have become established since 1980. Specifically, we used logistic models for binary outcomes, decoupled the definition of adherence from loss to follow-up, and adjusted for pre-randomization covariates via standardization and for post-randomization covariates via inverse probability weighting. Results: The original Coronary Drug Project analysis reported a difference in 5-year mortality between adherers and non-adherers in the placebo arm of 9.4 percentage points. Using modern approaches, we found that this difference was reduced to 2.5 (95% confidence interval: −2.1 to 7.0). Conclusion: Valid estimation of per-protocol effects may be possible in randomized clinical trials when analysts use appropriate methods to adjust for post-randomization variables.
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Data from a randomised trial can be used to derive an unbiased estimate of the effect of adhering to intervention.
“Data from a randomised trial can be used to derive an unbiased estimate of the effect of adhering to intervention. 19 20”
From RoB 2: a revised tool for assessing risk of bias in randomised trials (2019) · cites this paper as [19]
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Additional file 1 of Per-protocol analysis of the ZINC trial for HIV disease among alcohol users
Lodi, S., Freiberg, M. S., Gnatienko, N., Blokhina, E. A., Yaroslavtseva, T., Krupitsky, E., Murray, E. J., Samet, J. H., & Cheng, D. M. (2021) · Figshare · doi:10.6084/m9.figshare.14274762
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- Authors
- Murray, E. J., & Hernán, M. A.
- Affiliations
- Harvard University (US)
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Corresponding author
- Murray, Eleanor J
- ORCID
- Murray, Eleanor J
- Venue
- Clinical Trials
- Publisher
- SAGE Publications
- Published
- 2016-03-07
- Volume
- 13
- Issue
- 4
- Pages
- 372-378
- ISSN
- 1740-7745 (ISSN-L)
- 1740-7753
- PMC
- PMC4942353
- OpenAlex
- W2295342558
- Cited by
- 57
- References
- 23
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 8 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 9 · 2019: 3 · 2018: 7 · 2017: 4 · 2016: 3
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Trials (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL080644)
- National Institute of Allergy and Infectious Diseases (R01AI102634)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- medication adherence, Coronary Drug Project, loss to follow-up, inverse probability weighting, standardization, intention-to-treat effect, 5-year mortality, replication
- MeSH terms
- Coronary Disease, Data Interpretation, Statistical, Humans, Male, Risk, Logistic Models, Randomized Controlled Trials as Topic, Treatment Outcome, Medication Adherence, Intention to Treat Analysis
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