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Cure fraction model with random effects for regional variation in cancer survival
Author(s) -
Seppä Karri,
Hakulinen Timo,
Kim HyonJung,
Läärä Esa
Publication year - 2010
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.4046
Subject(s) - random effects model , fraction (chemistry) , markov chain , statistics , markov chain monte carlo , gibbs sampling , bayesian probability , population , survival analysis , breast cancer , cancer , cancer registry , mathematics , econometrics , medicine , chemistry , meta analysis , environmental health , organic chemistry
Abstract Assessing regional differences in the survival of cancer patients is important but difficult when separate regions are small or sparsely populated. In this paper, we apply a mixture cure fraction model with random effects to cause‐specific survival data of female breast cancer patients collected by the population‐based Finnish Cancer Registry. Two sets of random effects were used to capture the regional variation in the cure fraction and in the survival of the non‐cured patients, respectively. This hierarchical model was implemented in a Bayesian framework using a Metropolis‐within‐Gibbs algorithm. To avoid poor mixing of the Markov chain, when the variance of either set of random effects was close to zero, posterior simulations were based on a parameter‐expanded model with tailor‐made proposal distributions in Metropolis steps. The random effects allowed the fitting of the cure fraction model to the sparse regional data and the estimation of the regional variation in 10‐year cause‐specific breast cancer survival with a parsimonious number of parameters. Before 1986, the capital of Finland clearly stood out from the rest, but since then all the 21 hospital districts have achieved approximately the same level of survival. Copyright © 2010 John Wiley & Sons, Ltd.