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Epidemiology

Multistate models for the natural history of cancer progression

British Journal of Cancer volume 127, pages 1279–1288 (2022)Cite this article

Subjects

Abstract

Background

Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies.

Methods

We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state.

Results

In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening.

Conclusions

Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.

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Fig. 1: Examples of multistate models.
Fig. 2: Examples of multistate cancer processes.
Fig. 3: Comparison of risk estimation approaches in data generated under various observation schemes.

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Code availability

All codes used for data simulation and analysis are available on Github (https://github.com/liccheung/multistate.model.simulations).

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Acknowledgements

This work utilised the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov).

Funding

This work was funded in part by the Intramural Research Program of the US National Institutes of Health (NIH)/National Cancer Institute.

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Authors and Affiliations

  1. Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA

    Li C. Cheung, Paul S. Albert & Shrutikona Das

  2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada

    Richard J. Cook

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LCC had full access to the data in the study and take final responsibility for the decision to submit it for publication. LCC, PSA, and RJC conceived and designed the work. LCC drafted the manuscript. LCC and SD created the figures. All authors participated in statistical analysis and interpretation and in critical revision of the manuscript.

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Correspondence to Li C. Cheung.

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Cheung, L.C., Albert, P.S., Das, S. et al. Multistate models for the natural history of cancer progression. Br J Cancer 127, 1279–1288 (2022). https://doi.org/10.1038/s41416-022-01904-5

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