A Bayesian Joint Modelling for Misclassified Interval-censoring and Competing Risks
arxiv(2024)
摘要
In active surveillance of prostate cancer, cancer progression is
interval-censored and the examination to detect progression is subject to
misclassification, usually false negatives. Meanwhile, patients may initiate
early treatment before progression detection, constituting a competing risk. We
developed the Misclassification-Corrected Interval-censored Cause-specific
Joint Model (MCICJM) to estimate the association between longitudinal
biomarkers and cancer progression in this setting. The sensitivity of the
examination is considered in the likelihood of this model via a parameter that
may be set to a specific value if the sensitivity is known, or for which a
prior distribution can be specified if the sensitivity is unknown. Our
simulation results show that misspecification of the sensitivity parameter or
ignoring it entirely impacts the model parameters, especially the parameter
uncertainty and the baseline hazards. Moreover, specification of a prior
distribution for the sensitivity parameter may reduce the risk of
misspecification in settings where the exact sensitivity is unknown, but may
cause identifiability issues. Thus, imposing restrictions on the baseline
hazards is recommended. A trade-off between modelling with a sensitivity
constant at the risk of misspecification and a sensitivity prior at the cost of
flexibility needs to be decided.
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