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<p>Scheike et al. propose a dynamic regression augmentation method to improve estimation of treatment effects on recurrent events using auxiliary covariates in randomized clinical trials. This discussion considers the causal interpretation of such effects within the potential outcomes framework. Marginal effects for the full randomized population are distinguished from effects among survivors, and conditioning on survival is noted to potentially introduce posttreatment selection when survival is influenced by treatment. These considerations complement the authors’ approach by clarifying the interpretation of survivor-based analyses when survival may be treatment-dependent.</p>

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Comments on: Randomized clinical trials and the proportional hazards model for recurrent events

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Yu-Jen Cheng

摘要

Scheike et al. propose a dynamic regression augmentation method to improve estimation of treatment effects on recurrent events using auxiliary covariates in randomized clinical trials. This discussion considers the causal interpretation of such effects within the potential outcomes framework. Marginal effects for the full randomized population are distinguished from effects among survivors, and conditioning on survival is noted to potentially introduce posttreatment selection when survival is influenced by treatment. These considerations complement the authors’ approach by clarifying the interpretation of survivor-based analyses when survival may be treatment-dependent.