Use of clone-censor-weight to avoid immortal-time bias: a systematic methodological review.

Authors

Aldrine Manzanilla, Clément Brunetta, François Peyre-Pradat, Stefan Michiels, Thomas Filleron, Noémie Simon-Tillaux

Published in

Journal of clinical epidemiology. Pages 112468. Aug 14, 2026. Epub Aug 14, 2026.

Abstract

Observational studies evaluating treatment strategies that are indistinguishable at the start of follow-up are prone to immortal-time bias, in its misclassification form. The clone-censor-weight (CCW) approach has gained attention for its effectiveness in mitigating immortal-time bias while enhancing the validity of causal inference in observational research within the target trial emulation framework. In this methodological systematic review, we assess CCW implementation in observational analyses, evaluate the handling of immortal-time bias and identify key considerations and potential limitations in CCW application.
We conducted a comprehensive search in MEDLINE and Embase databases for all studies published from January 1^st, 2010, to March 18^th, 2025. We performed an additional manual search by citation searching on nine reviews about the target trial emulation or the CCW method. Screening was conducted by two independent reviewers. Data extraction and evaluation of risk of bias were performed by two reviewers for 10% and due to high inter-reviewer agreement, a single reviewer evaluated the remaining study sample. Risk of immortal-time bias due to post-baseline eligibility was assessed.
Among 113 studies identified, 103 declared implementing the CCW approach within the target trial emulation framework (91.2%). The method was referred as "cloning censoring and weighting" in 55 studies (48.7%), "clone-censor-weight" in 25 studies (22.1%) or other ways. All studies used either inverse probability of censoring weighting (IPCW) or inverse probability of treatment weighting (IPTW) to account for artificial censoring alone (97/113, 85.8%) and less commonly in combination with loss to follow-up (16/113, 14.2%). Authors used pooled logistic regressions or Cox models for weights estimations (n=70, 61.9% and n=26, 23% of studies respectively), while outcomes were mostly estimated using pooled logistic regressions (n=58, 51.3%) or non-parametric estimators (n=40, 35.4%). Potential immortal-time bias was still present in 22 studies (19.5%) where they excluded patients based on post-time zero criteria before implementing CCW (n=21, 18.6%), or when time zero was not well defined (n=2, 1.8%).
CCW implementation varies across studies, particularly in statistical modeling of weights, and considered censoring events, as well as statistical modeling effect of interventions on outcomes. Clarifying key methodological components could support consistent application of CCW and improve reporting.

PMID:
42600711
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

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