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Nonparametric estimation of the hazard function by using a model selection method: estimation of cancer deaths in Hiroshima atomic bomb survivors
Author(s) -
MartinMagniette M. L.
Publication year - 2005
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2005.00486.x
Subject(s) - akaike information criterion , pointwise , statistics , nonparametric statistics , confidence interval , estimation , mathematics , hazard , hazard ratio , absolute risk reduction , survival function , function (biology) , selection (genetic algorithm) , model selection , econometrics , survival analysis , computer science , chemistry , artificial intelligence , mathematical analysis , management , organic chemistry , evolutionary biology , economics , biology
Summary. Controversy has intensified regarding the death‐rate from cancer that is induced by a dose of radiation. In the models that are usually considered the hazard function is an increasing function of the dose of radiation. Such models can mask local variations. We consider the models of excess relative risk and of absolute risk and propose a nonparametric estimation of the effect of the dose by using a model selection procedure. This estimation deals with stratified data. We approximate the function of the dose by a collection of splines and select the best one according to the Akaike information criterion. In the same way between the models of excess relative risk or excess absolute risk, we choose the model that best fits the data. We propose a bootstrap method for calculating a pointwise confidence interval of the dose function. We apply our method for estimating the solid cancer and leukaemia death hazard functions to Hiroshima.