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Correlated multistate models for multiple processes: an application to renal disease progression in systemic lupus erythematosus

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  • Aidan G. O’Keeffe
  • Li Su
  • Vernon T. Farewell

Abstract

Bidirectional changes over time in the estimated glomerular filtration rate and in urine protein content are of interest for the treatment and management of patients with lupus nephritis. Although these processes may be modelled by separate multistate models, the processes are likely to be correlated within patients. Motivated by the lupus nephritis application, we develop a new multistate modelling framework where subject‐specific random effects are introduced to account for the correlations both between the processes and within patients over time. Models are fitted by using bespoke code in standard statistical software. A variety of forms for the random effects are introduced and evaluated by using the data from the Systemic Lupus International Collaborating Clinics.

Suggested Citation

  • Aidan G. O’Keeffe & Li Su & Vernon T. Farewell, 2018. "Correlated multistate models for multiple processes: an application to renal disease progression in systemic lupus erythematosus," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 841-860, August.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:4:p:841-860
    DOI: 10.1111/rssc.12257
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    Cited by:

    1. Giorgos Bakoyannis, 2021. "Nonparametric analysis of nonhomogeneous multistate processes with clustered observations," Biometrics, The International Biometric Society, vol. 77(2), pages 533-546, June.

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