Time Partitioning in Target Trial Emulation

2026-03-16

AI Summary

针对目标试验模拟中的时间划分问题，提出基于因果模型的方法，指导合理设定时间粒度以避免偏差和维度灾难。

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In target trial emulation, time partitioning enables researchers to handle time-varying confounders and immortal time bias with appropriate methods. Based on two clinical scenarios, this study aimed to explore issues related to time partitioning and to provide guidance for trial emulation. After formalizing the research question within the framework of structural causal models, we show how a given time partitioning may be too fine or too coarse depending on the clinical context. When the partitioning is too fine, the dimensionality of the model is unnecessarily high. When the partitioning is too coarse, the resulting causal structure may hinder effect estimation. We also show that cloning-censoring-weighting may not be valid when treatment influences outcome within study periods, and we confirm this through simulations. In conclusion, we provide practical guidance for actively specifying an appropriate time partitioning in trial emulation, rather than using the available data resolution as a default.

Problem

• time partitioning
• target trial emulation
• time-varying confounders
• immortal time bias
• causal effect estimation

Innovation

• time partitioning
• target trial emulation
• causal inference
• immortal time bias
• cloning-censoring-weighting