A joint model of cancer incidence, metastasis, and mortality

Qui Tran (), Kelley M. Kidwell () and Alex Tsodikov ()
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Qui Tran: University of Michigan
Kelley M. Kidwell: University of Michigan
Alex Tsodikov: University of Michigan

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 3, No 1, 385-406

Abstract: Abstract Many diseases, especially cancer, are not static, but rather can be summarized by a series of events or stages (e.g. diagnosis, remission, recurrence, metastasis, death). Most available methods to analyze multi-stage data ignore intermediate events and focus on the terminal event or consider (time to) multiple events as independent. Competing-risk or semi-competing-risk models are often deficient in describing the complex relationship between disease progression events which are driven by a shared progression stochastic process. A multi-stage model can only examine two stages at a time and thus fails to capture the effect of one stage on the time spent between other stages. Moreover, most models do not account for latent stages. We propose a semi-parametric joint model of diagnosis, latent metastasis, and cancer death and use nonparametric maximum likelihood to estimate covariate effects on the risks of intermediate events and death and the dependence between them. We illustrate the model with Monte Carlo simulations and analysis of real data on prostate cancer from the SEER database.

Keywords: Disease natural history; Marked endpoints; Competing risks; Semiparametric regression; Survival analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9407-2

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