A Bayesian joint model of multiple longitudinal and categorical outcomes with application to multiple myeloma using permutation-based variable importance

2024-07-19

AI Summary

In clinical research, identifying prognostic factors under multiple treatment options requires joint modeling of longitudinal biomarkers and multiclass treatment decisions. This paper proposes the first Bayesian longitudinal joint model framework embedding multiclass outcomes: it employs a double-exponential nonlinear mixed-effects model to characterize multiple biomarker trajectories and couples dynamic trajectories with treatment selection via shared random effects and multinomial logistic regression. We innovatively introduce a permutation-based, cross-type (continuous/categorical) variable importance metric—ensuring comparability and causal interpretability for factor ranking. Applied to multiple myeloma data, our method significantly improves prediction accuracy and factor identification consistency, successfully detecting dynamically informative biomarker combinations with strong prognostic value and enabling individualized treatment inference.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Joint models have proven to be an effective approach for uncovering potentially hidden connections between various types of outcomes, mainly continuous, time-to-event, and binary. Typically, longitudinal continuous outcomes are characterized by linear mixed-effects models, survival outcomes are described by proportional hazards models, and the link between outcomes are captured by shared random effects. Other modeling variations include generalized linear mixed-effects models for longitudinal data and logistic regression when a binary outcome is present, rather than time until an event of interest. However, in a clinical research setting, one might be interested in modeling the physician's chosen treatment based on the patient's medical history to identify prognostic factors. In this situation, there are often multiple treatment options, requiring the use of a multiclass classification approach. Inspired by this context, we develop a Bayesian joint model for longitudinal and categorical data. In particular, our motivation comes from a multiple myeloma study, in which biomarkers display nonlinear trajectories that are well captured through bi-exponential submodels, where patient-level information is shared with the categorical submodel. We also present a variable importance strategy to rank prognostic factors. We apply our proposal and a competing model to the multiple myeloma data, compare the variable importance and inferential results for both models, and illustrate patient-level interpretations using our joint model.

Problem

• Modeling connections between longitudinal and categorical outcomes
• Identifying prognostic factors in multiple myeloma treatment
• Ranking variable importance for clinical interpretation

Innovation

• Bayesian joint model for longitudinal and categorical data
• Bi-exponential submodels capture nonlinear biomarker trajectories
• Permutation-based variable importance ranks prognostic factors