Spatial dependence modeling of latent susceptibility and time to joint damage in psoriatic arthritis

Important scientific insights into chronic diseases affecting several organ systems can be gained from modeling spatial dependence of sites experiencing damage progression. We describe models and methods for studying spatial dependence of joint damage in psoriatic arthritis (PsA). Since a large number of joints may remain unaffected even among individuals with a long disease history, spatial dependence is first modeled in latent joint-specific indicators of susceptibility. Among susceptible joints, a Gaussian copula is adopted for dependence modeling of times to damage. Likelihood and composite likelihoods are developed for settings, where individuals are under intermittent observation and progression times are subject to type K interval censoring. Two-stage estimation procedures help mitigate the computational burden arising when a large number of processes (i.e., joints) are under consideration. Simulation studies confirm that the proposed methods provide valid inference, and an application to the motivating data from the University of Toronto Psoriatic Arthritis Clinic yields important insights which can help physicians distinguish PsA from arthritic conditions with different dependence patterns.

Automated summary

Modeling the spatial dependence of damage progression in chronic diseases can provide important scientific insights into their effects on multiple organ systems. This study focuses on modeling the spatial dependence of joint damage in psoriatic arthritis (PsA) and proposes new models and methods. By incorporating latent joint-specific indicators of susceptibility and adopting a Gaussian copula for dependence modeling, the authors develop likelihood and composite likelihoods for analyzing progression times subject to interval censoring. Simulation studies confirm the validity of the proposed methods, and the application to real data provides important insights for distinguishing PsA from other arthritic conditions.