Longitudinal Latent Overall Toxicity (LOTox) profiles in osteosarcoma: a new taxonomy based on latent Markov models

TL;DR

This paper introduces a new methodology using latent Markov models and compositional data techniques to analyze longitudinal toxicity data in osteosarcoma, aiming to identify toxicity states and their evolution for better clinical decision-making.

Contribution

It presents a novel approach combining latent Markov models with compositional data analysis to classify and track toxicity profiles over time in cancer patients.

Findings

Identification of distinct latent toxicity states in osteosarcoma patients.

Insights into the evolution of toxicity risk during treatment.

Potential support for personalized medical decisions.

Abstract

Due to the presence of multiple types of adverse events with different levels of severity, the analysis of longitudinal toxicity data is a difficult task in cancer studies. In this work, a novel approach based on latent Markov models and compositional data techniques is proposed. The latent status of interest is the Latent Overall Toxicity (LOTox) condition of each patient. The main objectives consist in identifying different latent states of overall toxicity burden and investigating the evolution of individual toxicity risk during cancer treatment. This methodology is applied to osteosarcoma treatment data to provide novel techniques that may support medical decisions in childhood cancer therapy.