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Updated: Jun 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Estimating absolute risks in the presence of nonadherence: an application to a follow-up study with baseline
Sengwee Toh1, Sonia Hernández-Díaz, Roger Logan
1Department of Epidemiology, Harvard School of Public Health, Boston, MA 02215, USA. darrentoh@post.harvard.edu
Abstract:
The intention-to-treat (ITT) analysis provides a valid test of the null hypothesis and naturally results in both absolute and relative measures of risk. However, this analytic approach may miss the occurrence of serious adverse effects that would have been detected under full adherence to the assigned treatment. Inverse probability weighting of marginal structural models has been used to adjust for nonadherence, but most studies have provided only relative measures of risk. In this study, we used inverse probability weighting to estimate both absolute and relative measures of risk of invasive breast cancer under full adherence to the assigned treatment in the Women's Health Initiative estrogen-plus-progestin trial. In contrast to an ITT hazard ratio (HR) of 1.25 (95% confidence interval [CI] = 1.01 to 1.54), the HR for 8-year continuous estrogen-plus-progestin use versus no use was 1.68 (1.24 to 2.28). The estimated risk difference (cases/100 women) at year 8 was 0.83 (-0.03 to 1.69) in the ITT analysis, compared with 1.44 (0.52 to 2.37) in the adherence-adjusted analysis. Results were robust across various dose-response models. We also compared the dynamic treatment regimen "take hormone therapy until certain adverse events become apparent, then stop taking hormone therapy" with no use (HR = 1.64; 95% CI = 1.24 to 2.18). The methods described here are also applicable to observational studies with time-varying treatments.
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