Time Partitioning in Target Trial Emulation

TL;DR

This paper discusses how to choose appropriate time partitions in target trial emulation to accurately handle time-varying confounders and avoid biases, providing practical guidance based on causal modeling and simulations.

Contribution

It offers a formal framework and practical guidance for selecting suitable time partitions in trial emulation, addressing issues of granularity and validity of methods.

Findings

Overly fine partitions increase model complexity unnecessarily.

Overly coarse partitions can hinder effect estimation.

Cloning-censoring-weighting may be invalid when treatment affects outcomes within periods.

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.