Publication: Adherence adjustment in the Coronary Drug Project: A call for better per-protocol effect estimates in randomized trials
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Date
2016
Published Version
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SAGE Publications
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Citation
Murray, Eleanor J, and Miguel A Hernán. 2016. “Adherence Adjustment in the Coronary Drug Project: A Call for Better Per-Protocol Effect Estimates in Randomized Trials.” Clinical Trials: Journal of the Society for Clinical Trials 13 (4) (March 7): 372–378. doi:10.1177/1740774516634335.
Research Data
Abstract
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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Keywords
Coronary Drug Project, Per-protocol effect, adherence, intention-to-treat effect, inverse probability weighting
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