Estimating treatment duration effects via clone-censor-weight: a breast cancer case study

TL;DR

This paper evaluates the clone-censor-weight framework for estimating treatment duration effects in observational survival data, addressing methodological challenges and applying it to breast cancer treatment strategies.

Contribution

It formalizes assumptions underlying CCW, compares estimation methods via simulations, and demonstrates its application to real breast cancer data with insights on limitations.

Findings

CCW can emulate target trials for treatment duration effects.

Simulation studies reveal robustness and sensitivity of estimators.

Application to breast cancer data shows practical relevance and limitations.

Abstract

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.