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A multistate survival model of the natural history of cancer using data from screened and unscreened population
Author(s) -
Bhatt Rikesh,
den Hout Ardo,
Pashayan Nora
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8998
Subject(s) - statistics , natural history , cancer , proportional hazards model , population , prostate cancer , markov chain , survival analysis , markov model , medicine , computer science , mathematics , environmental health
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.