Structural Limitations of Early–Late Treatment Comparisons Under Target Trial Emulation: A Simulation Study

Early versus Late (E-L) treatment initiation comparisons, which are prone to immortal time bias, are widely considered amenable to causal inference through target trial emulation (TTE) using clone-censor-weighting (CCW). However, whether standard CCW yields a well-defined causal estimand for the E-L comparison under ideal implementation conditions has not been formally examined. We conducted a structural simulation study to evaluate CCW-based TTE for E-L comparisons. The design varied along three dimensions: treatment-initiation mechanisms (random versus baseline-confounded timing), benefit-harm structures governing delayed-treatment harm and post-treatment benefit, and five distinct E-L initiation-window configurations. We implemented CCW as conventionally recommended and summarized estimated 12-month marginal risk ratios across settings. Depending on the placement and structure of the initiation windows, estimated risk ratios exhibited systematic window-dependent variation, including direction reversals across window specifications, even in settings where the true marginal causal contrast was 1 by design. These patterns were not consistently explained by weight instability or lack of support overlap. Our findings indicate that, in E-L comparisons, the window-defined strategy contrast estimated using CCW-based TTE can be strongly shaped by risk accumulated before treatment initiation under the specified windows, underscoring the need for caution when interpreting E-L analyses.

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Publication Details

Journal
American Journal of Epidemiology
Published
2026-08-28
DOI
https://doi.org/10.1093/aje/kwag208
Primary Topic
Advanced Causal Inference Techniques
Type
article
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article

Structural Limitations of Early–Late Treatment Comparisons Under Target Trial Emulation: A Simulation Study

Koji Kawakami, Atsushi Takayama, Shiro Tanaka
American Journal of Epidemiology
Advanced Causal Inference Techniques
article

Structural Limitations of Early–Late Treatment Comparisons Under Target Trial Emulation: A Simulation Study

Koji Kawakami, Atsushi Takayama, Shiro Tanaka
article en

Abstract

Early versus Late (E-L) treatment initiation comparisons, which are prone to immortal time bias, are widely considered amenable to causal inference through target trial emulation (TTE) using clone-censor-weighting (CCW). However, whether standard CCW yields a well-defined causal estimand for the E-L comparison under ideal implementation conditions has not been formally examined. We conducted a structural simulation study to evaluate CCW-based TTE for E-L comparisons. The design varied along three dimensions: treatment-initiation mechanisms (random versus baseline-confounded timing), benefit-harm structures governing delayed-treatment harm and post-treatment benefit, and five distinct E-L initiation-window configurations. We implemented CCW as conventionally recommended and summarized estimated 12-month marginal risk ratios across settings. Depending on the placement and structure of the initiation windows, estimated risk ratios exhibited systematic window-dependent variation, including direction reversals across window specifications, even in settings where the true marginal causal contrast was 1 by design. These patterns were not consistently explained by weight instability or lack of support overlap. Our findings indicate that, in E-L comparisons, the window-defined strategy contrast estimated using CCW-based TTE can be strongly shaped by risk accumulated before treatment initiation under the specified windows, underscoring the need for caution when interpreting E-L analyses.

American Journal of Epidemiology
Kyoto University (JP), Kyushu University Hospital (JP)
Peace, Justice and strong institutions
Openalex Percentile: Top 7%
Advanced Causal Inference Techniques
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Structural Limitations of Early–Late Treatment Comparisons Under Target Trial Emulation: A Simulation Study — Koji Kawakami, Atsushi Takayama, et al. · American Journal of Epidemiology (2026) | TGRS Research Map | TGRS