title: Additive models for geo-referenced failure time data.
abstract: Asthma researchers have found some evidence that geographical variations in susceptibility to asthma could reflect the effect of community level factors such as exposure to violence. Our methodology was motivated by a study of age at onset of asthma among children of inner-city neighbourhoods in East Boston. Cox's proportional hazards model was not appropriate since there was not enough information about the nature of geographical variations so as to impose a parametric relationship. In addition, some of the known risk factors were believed to have non-linear log-hazard ratios. We extend the geoadditive models of Kamman and Wand to the case where the outcome measure is a possibly censored time to event. We reduce the problem to one of fitting a Poisson mixed model by using Poisson approximations in conjunction with a mixed model formulation of generalized additive modelling. Our method allows for low-rank additive modelling, provides likelihood-based estimation of all parameters including the amount of smoothing and can be implemented using standard software. We illustrate our method on the East Boston data.
authors.affiliation: Department of Statistics, University of Calcutta, Kolkata, India. bgstat@caluniv.ac.in
authors.forename: B
authors.initials: B
authors.lastname: Ganguli
authors.forename: M P
authors.initials: MP
authors.lastname: Wand
mesh_terms: D001249:Asthma / Q000453:epidemiology* / Q000517:prevention & control; D001900:Boston / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D016012:Poisson Distribution; D011204:Poverty Areas; D012111:Residence Characteristics; D012307:Risk Factors; D018709:Statistics, Nonparametric; D019371:Topography, Medical / Q000706:statistics & numerical data*; D014505:Urban Population