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

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 (TTE) 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, 2010, to March 18, 2025. We performed an additional manual search by citation searching on nine reviews about the TTE 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 TTE framework (91.2%). The method was referred as "cloning censoring and weighting" in 55 studies (48.7%), "CCW" in 25 studies (22.1%), or other ways. All studies used either inverse probability of censoring weighting or inverse probability of treatment weighting 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 weight 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 nonparametric 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.

Researchers increasingly use observational health-care data, such as electronic health records and insurance claims, to evaluate the effects of medical treatments. Unlike randomized clinical trials, these studies can be affected by immortal-time bias if both intervention assignment and eligibility assessment are not set at the start of follow-up. This bias typically occurs when patients must survive long enough to receive a treatment, such as surgery. Patients who die before receiving the surgery are automatically classified as untreated, leading to an unfair comparison. The clone-censor-weight (CCW) approach was developed to reduce this bias in observational studies, with interventions that cannot be distinguished at the start of follow-up. In this paper, we reviewed 113 published studies using this approach to evaluate how researchers implemented the method, whether immortal-time bias was appropriately addressed, and how clearly key methodological details were reported. We found variation in the implementation of the CCW approach, particularly in censoring and weighting processes. Important methodological details were also often incompletely reported, making it difficult to compare studies or reproduce their analyses. Despite the use of CCW approach, 20% of the evaluated studies remained at risk of immortal-time bias, due to eligibility assessment based on characteristics measured after the start of follow-up. Our findings highlight the need for clearer methodological guidance and more transparent reporting to improve the consistency and reliability of studies using the CCW approach.