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Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation

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Emerging Topics in Modeling Interval-Censored Survival Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

A wealth of information is available from clinic-based registries of individuals with chronic diseases but modeling and analysis of disease processes can be challenging because of the ways individuals are enrolled in the registry and because they are only seen at intermittent clinic visits post-enrolment. Such features are common in observational cohort studies in general, but even in randomized trials, intermittent observation of individuals is common, resulting in incomplete information about transitions among disease states and about time-dependent covariates. In this chapter we describe independence conditions needed for valid likelihood-based inference about multistate disease processes under intermittent observation schemes. In addition, we describe how joint models for disease and observation processes can be used to address disease-related clinic visits. We also describe how joint models can be used to deal with internal time-dependent markers when marker values are observed only at clinic visits, and investigate the limiting values of regression coefficients of marker effects when the common approach of carrying forward the most recently recorded value is used. In addition to failure time processes, we consider more general multistate disease processes, and partially specified models for state occupancy probabilities. An application to a study of cancer metastatic to bone is considered, involving a serum marker of bone activity and its association with risk of skeletal complications.

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Acknowledgements

Funding was provided by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-04207 for RJC and RGPIN-2017-04055 for JFL). Richard Cook is a Mathematics Faculty Research Chair. The authors thank Novartis for permission to use the data from the cancer clinical trial for illustration and Ker-Ai Lee for help in the scientific computing and data analysis.

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Authors and Affiliations

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

    Richard J. Cook & Jerald F. Lawless

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  1. Richard J. Cook
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  2. Jerald F. Lawless
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Correspondence to Richard J. Cook .

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Editors and Affiliations

  1. Department of Statistics, University of Missouri, Columbia, MO, USA

    Jianguo Sun

  2. College of Health Solutions, Arizona State University, Goodyear, AZ, USA

    Ding-Geng Chen

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Cook, R.J., Lawless, J.F. (2022). Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation. In: Sun, J., Chen, DG. (eds) Emerging Topics in Modeling Interval-Censored Survival Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-12366-5_10

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