Statistical Modelling 9 (2009), 119135
Hierarchical dynamic time-to-event models for post-treatment preventive
care data on breast cancer survivors
Freda W Cooner, Xinhua Yu, Sudipto Banerjee,
Patricia L Grambsch, A Marshall McBean
Division of Biostatistics,
School of Public Health,
University of Minnesota
U.S.A.
Banerjee's eMail:
sudiptob@biostat.umn.edu
Abstract:
This paper considers modelling data arising in post-treatment preventive
care settings, where cancer patients who have undergone disease-directed
treatment discontinue seeking preventive care services. Clinicians and
public health researchers are interested in explaining such behavioural
patterns by modelling the time-to-receiving care while accounting for
several patient and treatment attributes. A key feature of such data is
that a noticeable number of patients would never return for screening,
a concept subtly different from censoring, where an individual does not
return for screening in the given time frame of the study. Models
distinguishing between these two concepts are known as cure rate models
and are often preferred for data where a significant part of the
population never experienced the endpoint. Building upon recent work on
hierarchical cure model framework we propose modelling a sequence of
latent events with a piecewise exponential distribution that remedies
oversmoothing encountered in existing models with different latent
distributions. We investigate simultaneous regression on the cure
fraction and the latent event distribution and derive a flexible class
of semiparametric cure rate models.
Keywords:
Cure rate models; dynamic survival models; hierarchical models;
latent activation schemes; Markov chain Monte Carlo; preventive care data
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