In this work, we study the estimation of treatment duration effects in observational survival data, where treatment and covariate histories evolve over time and longer observed durations are only attainable among individuals who remain event-free and under follow-up, leading to immortal time bias under naive analyses. The cloning-censoring-weighting (CCW) framework provides a practical approach to emulate target trials of treatment duration strategies, but several methodological aspects remain insufficiently understood.
We focus on static treatment duration strategies under two settings of increasing complexity: baseline confounding only, and confounding with time-varying covariates. We formalize the assumptions underlying CCW, with particular emphasis on treatment admissibility, relaxed intervention rules, and the distinction between artificial and natural censoring. We then compare several estimation approaches after cloning and censoring, including inverse probability of censoring weighting (IPCW), the G-formula, and doubly robust estimators, through simulation studies assessing robustness, variability, and sensitivity to censoring model misspecification.
Finally, we apply the framework to a Breast Cancer cohort to emulate a target trial comparing 2 versus 5 years of adjuvant tamoxifen in early stage breast cancer. Due to the small number of events and limited support for the 2-year strategy, estimates are associated with substantial uncertainty. These findings highlight both the practical relevance and the limitations of CCW, and underscore the importance of sensitivity analyses in complex longitudinal observational settings.
Formalization and consistency of clone-censor-weight
UMR MIA Paris-Saclay, AgroParisTech, INRAE, U. Paris-Saclay（巴黎萨克雷大学农业食品科学高等学院、法国国家农业食品与环境研究院）；Laboratoire Servier（赛诺菲实验室）；Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, Inserm, U. Paris-Saclay（巴黎萨克雷大学、INSERM、Oncostat U1018、古斯塔夫·鲁西癌症中心生物统计与流行病学办公室）；R&D Unicancer（Unicancer研发部）；U. Paris-Saclay, UVSQ, Inserm, CESP U1018, Cancer Survivorship Group, Gustave Roussy（巴黎萨克雷大学、凡尔赛圣康坦大学、INSERM、CESP U1018、古斯塔夫·鲁西癌症幸存者研究组）；PreMeDICaL, Inria-Inserm, U. Montpellier, France（法国蒙彼利埃大学、Inria-INSERM联合实验室PreMeDICaL）；U. Paris Cité and U. Sorbonne Paris Nord, INSERM, CRESS（巴黎西岱大学、北巴黎索邦大学、INSERM、CRESS）
Seeing Below the Limit of Detection: A Censored-Poisson Bayesian Latent-Growth Change-Point Detector (the Span Detector) for Serial ctDNA in HR+/HER2- Metastatic Breast Cancer
Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data
Division of Cancer Epidemiology and Genetics, National Cancer Institute（癌症流行病学与遗传学系，国家癌症研究所）；Department of Biostatistics, University of Florida（生物统计学系，佛罗里达大学）
De-biased Distribution Estimation for Locally Differential Private Survival Data
The State University of New York (SUNY) at Buffalo（纽约州立大学布法罗分校）；National Chengchi University（国立政治大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Estimating conditional Mann-Whitney effects using pseudo-observation-based regression
Institute of Statistics and Mathematical Economics, RWTH Aachen University（统计与数理经济研究所，亚琛工业大学）；Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, University Hospital Bonn（波恩大学医学院生物统计学、信息学与流行病学研究所，波恩大学医院）
BITES: Balanced Individual Treatment Effect for Survival data
University Medical Center Göttingen（哥廷根大学医学中心）；University of Regensburg（雷根斯堡大学）；Kiel University（基尔大学）；University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）
A parametric framework for assessing and estimating sufficient follow-up time in cure models
Federal University of São Carlos（圣卡洛斯联邦大学）；Institute of Mathematics and Computer Sciences（数学与计算机科学学院）；University of São Paulo（圣保罗大学）；MRC Biostatistics Unit（医学研究委员会生物统计单元）；University of Cambridge（剑桥大学）
