A Comparison of Two Approaches to Dynamic Prediction: Joint Modeling and Landmark Modeling

Authors

Wenhao Li
Liang Li
Brad C Astor

Publication Date

6-15-2023

Journal

Statistics in Medicine

DOI

10.1002/sim.9713

PMID

36938960

PMCID

PMC11809276

PubMedCentral® Posted Date

2-10-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Joint modeling and landmark modeling are two mainstream approaches to dynamic prediction in longitudinal studies, that is, the prediction of a clinical event using longitudinally measured predictor variables available up to the time of prediction. It is an important research question to the methodological research field and also to practical users to understand which approach can produce more accurate prediction. There were few previous studies on this topic, and the majority of results seemed to favor joint modeling. However, these studies were conducted in scenarios where the data were simulated from the joint models, partly due to the widely recognized methodological difficulty on whether there exists a general joint distribution of longitudinal and survival data so that the landmark models, which consists of infinitely many working regression models for survival, hold simultaneously. As a result, the landmark models always worked under misspecification, which caused difficulty in interpreting the comparison. In this paper, we solve this problem by using a novel algorithm to generate longitudinal and survival data that satisfies the working assumptions of the landmark models. This innovation makes it possible for a "fair" comparison of joint modeling and landmark modeling in terms of model specification. Our simulation results demonstrate that the relative performance of these two modeling approaches depends on the data settings and one does not always dominate the other in terms of prediction accuracy. These findings stress the importance of methodological development for both approaches. The related methodology is illustrated with a kidney transplantation dataset.

Keywords

Humans, Computer Simulation, Longitudinal Studies, Models, Statistical, Dynamic prediction, Joint model of longitudinal data and survival data, Landmark analysis, Partly conditional model, Survival analysis

Published Open-Access

yes

Recommended Citation

Wenhao Li, Liang Li, and Brad C Astor, "A Comparison of Two Approaches to Dynamic Prediction: Joint Modeling and Landmark Modeling" (2023). Faculty, Staff and Student Publications. 4733.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/4733