An important aim in cancer research is to study how treatment and prognostic factors influence the course of disease of a patient. Typically in these trials, besides overall survival, also other endpoints such as recurrence or distant metastasis are of interest. Usually, in these situations, Cox regression models are applied for each of these endpoints separately. These approaches, however, fail to give insight into what happens to a patient after the first event. The multistate models are flexible tools for analyzing time to event with multiple events. Here we used multistate models for the data of head and neck cancer with two transient states healthy, recurrence and one absorbing state death. The median survival time for these patients was 74.8 months and the 5-year survival rate was 52.3%. Based on the results of multistate model, the covariates primary site, treatment and stage of disease were affected the death hazard without recurrence and the primary site of tumor got identified as the factors affecting death hazard in patients with recurrence. This model is able to estimate the transition probabilities in different states of disease. A patient with head and neck cancer would develop recurrence with a probability of 13% within one year after completing the initial treatment.

survival analysis, multistate model, transition probability, head and neck cancer data.

Received: November 1, 2021; Accepted: December 18, 2021; Published: January 14, 2022

How to cite this article: T. Bindu, M. Kumaran and Saina Sunilkumar, Application of multistate model in analyzing head and neck cancer data, JP Journal of Biostatistics 19 (2022), 37-56. DOI: 10.17654/0973514322003

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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