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A multistate survival model of the natural history of cancer using data from screened and unscreened population

Bhatt, R; Van Den Hout, A; Pashayan, N; (2021) A multistate survival model of the natural history of cancer using data from screened and unscreened population. Statistics in Medicine 10.1002/sim.8998. (In press). Green open access

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Abstract

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen‐detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous‐time Markov model and the corresponding likelihood function. The model allows for the use of interval‐censored, left‐truncated and right‐censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age‐varying hazards and age‐varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro‐simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately.

Type: Article
Title: A multistate survival model of the natural history of cancer using data from screened and unscreened population
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.8998
Publisher version: https://doi.org/10.1002/sim.8998
Language: English
Additional information: © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: cancer screening, lead time, Markov model, misclassification, prostate cancer, sojourn time, time-varying hazard
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Applied Health Research
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10127367
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