ArticleClinical epidemiology2026
When to Start, How to Classify, and Whom to Include: A Methodological Assessment and Illustrative Example of the Clone-Censor-Weight Approach for Evaluating Intervention Timing in Observational Studies.
Article in Clinical epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Observational studies evaluating the effect of timing of an intervention, such as early versus deferred drug treatment initiation or short versus long treatment duration, often suffer from bias due to misalignment between eligibility assessment, intervention assignment, and start of follow-up. The clone-censor-weight approach aligns these time points and allows for estimation of treatment effects under predefined timing strategies. Methods: We assess the clone-censor-weight methodology and illustrate its application in an observational registry-based study of individuals with newly diagnosed diabetes, comparing infection risks under four strategies for initiation of glucose-lowering treatment: initiation within 15 days (Strategy 1), 15 days to 3 months (Strategy 2), 3 to 6 months (Strategy 3), or 6 to 12 months (Strategy 4) after diagnosis. Results: The clone-censor-weight approach established the diabetes diagnosis as the index date across all strategies. The study example included 33,857 individuals with newly diagnosed diabetes. They were cloned and assigned to all four treatment strategies, where 6439 (19.0%) clones adhered to Strategy 1, 8419 (24.9%) to Strategy 2, 5550 (16.2%) to Strategy 3, and 7539 (22.3%) to Strategy 4. The weighted absolute 5-year risks of infection were 67.0% (95% CI 65.3-68.5) for Strategy 1, 65.7% (95% CI 64.1-66.9) for Strategy 2, 66.8% (95% CI 61.8-71.9) for Strategy 3, and 67.8% (95% CI 62.8-72.7) for Strategy 4. Discussion: We have assessed the methodology and illustrated the clone-censor-weight approach in a study example evaluating intervention timing. Censoring weights should account for selection bias, estimating per-protocol effects under full adherence, but methodological challenges remain in ensuring that the weights adequately reflect both the target intervention strategies and the population they are intended to represent.
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