# Clustered multi-state models with observation-level random effects, mover-stayer effects and dynamic covariates: Modelling transition intensities and sojourn times in a study of psoriatic arthritis

## TL;DR

This paper develops a sophisticated multi-state modeling approach for psoriatic arthritis, capturing joint activity and damage processes at the individual joint level, incorporating random effects, dynamic covariates, and subpopulation effects.

## Contribution

It introduces a novel clustered multi-state model with observation-level random effects, dynamic covariates, and mover-stayer effects, enhancing understanding of joint activity and damage relationships.

## Key findings

- Improved modeling of joint activity and damage over time.
- Identification of subpopulations at minimal risk of damage.
- Enhanced interpretability of covariate effects on sojourn times and transition probabilities.

## Abstract

In psoriatic arthritis, it is important to understand the joint activity (represented by swelling and pain) and damage processes because both are related to severe physical disability. This paper aims to provide a comprehensive investigation in to both processes occurring over time, in particular their relationship, by specifying a joint multi-state model at the individual hand joint-level, which also accounts for many of their important features. As there are multiple hand joints, such an analysis will be based on the use of clustered multi-state models. Here we consider an observation-level random effects structure with dynamic covariates and allow for the possibility that a subpopulation of patients are at minimal risk of damage. Such an analysis is found to provide further understanding of the activity-damage relationship beyond that provided by previous analyses. Consideration is also given to the modelling of mean sojourn times and jump probabilities. In particular, a novel model parameterization which allows easily interpretable covariate effects to act on these quantities is proposed.