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Multistate models for examining the progression of intermittently-measured patient-reported symptoms among cancer patients: the importance of accounting for interval censoring

Sutradhar R, Barbera L. J Pain Symptom Manage. 2020; Jul 17 [Epub ahead of print]. DOI: https://doi.org/10.1016/j.jpainsymman.2020.07.012


Purpose — Cancer patients in Ontario, Canada, receive symptom monitoring in a standardized fashion using the Edmonton Symptom Assessment System (ESAS). This paper demonstrates the implementation of multistate models for examining symptom progression, while appropriately accounting for intermittent observation. We also compare the estimates when the panel nature of the data is ignored.

Methods — This was a population-based retrospective cohort study using linked administrative healthcare databases. The cohort consisted of patients who were newly diagnosed with a primary cancer and had at least one ESAS assessment completed between 2007 and 2015 in Ontario, Canada. A 5-state model was developed to examine the progression of symptom severity, where estimation was conducted with and without accommodating for the panel nature of the symptom data.

Results — The study cohort consisted of 212,615 patients diagnosed with cancer, collectively having 1,006,360 ESAS assessments within the first year after diagnosis. The median (IQR) of the number of ESAS assessments per patient was 3 (1–6), and the average gap time between consecutive assessments was approximately 3 months. The estimated mean sojourn time in each state was consistently and significantly greater when ignoring interval censoring compared to when accounting for it. This held true for all states and symptoms.

Conclusion — Our work demonstrates the use of multistate models and the importance of accommodating for intermittent observation when examining symptom progression using ESAS among patients with cancer. This work serves as a methodological guide for applied researchers interested in modeling disease progression under the presence of intermittent observation.

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