Statistical Methods for Accommodating Immortal Time: A Selective Review and Comparison

TL;DR

This paper reviews and compares statistical methods for handling immortal time bias in survival studies, highlighting the advantages of time-varying treatment models and sequential methods through simulations and real data analysis.

Contribution

It provides a comprehensive review and direct comparison of existing methods for immortal time bias, filling a gap in the literature and aiding biomedical researchers.

Findings

Time-varying treatment models and sequential methods tend to produce unbiased estimates.

Simpler methods may lead to substantial bias if not properly applied.

Simulation and real data analysis demonstrate the performance differences among methods.

Abstract

Epidemiologic studies and clinical trials with a survival outcome are often challenged by immortal time (IMT), a period of follow-up during which the survival outcome cannot occur because of the observed later treatment initiation. It has been well recognized that failing to properly accommodate IMT leads to biased estimation and misleading inference. Accordingly, a series of statistical methods have been developed, from the simplest by including or excluding IMT to various weightings and the more recent sequential methods. Our literature review suggests that the existing developments are often "scattered", and there is a lack of comprehensive review and direct comparison. To fill this knowledge gap and better introduce this important topic especially to biomedical researchers, we provide this review to comprehensively describe the available methods, discuss their advantages and disadvantages, and equally important, directly compare their performance via simulation and the analysis of the Stanford heart transplant data. The key observation is that the time-varying treatment modeling and sequential trial methods tend to provide unbiased estimation, while the other methods may result in substantial bias. We also provide an in-depth discussion on the interconnections with causal inference.