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Exploring spatio‐temporal patterns of mortality using mixed effects models
Author(s) -
Pickle Linda Williams
Publication year - 2000
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 - 10.1002/1097-0258(20000915/30)19:17/18<2251::aid-sim567>3.0.co;2-m
Subject(s) - statistics , overdispersion , estimator , poisson distribution , small area estimation , econometrics , random effects model , variance (accounting) , computer science , mathematics , count data , medicine , meta analysis , accounting , business
A linear mixed effects (LME) model previously used for a spatial analysis of mortality data for a single time period is extended to include time trends and spatio‐temporal interactions. This model includes functions of age and time period that can account for increasing and decreasing death rates over time and age, and achange‐point of rates at a predetermined age. A geographic hierarchy is included that provides both regional and small area age‐specific rate estimates, stabilizing rates based on small numbers of deaths by sharing information within a region. The proposed log‐linear analysis of rates allows the use of commercially available software for parameter estimation, and provides an estimator of overdispersion directly as the residual variance. Because of concerns about the accuracy of small area rate estimates when there are many instances of no observed deaths, we consider potential sources of error, focusing particularly on the similarity of likelihood inferences using the LME model for rates as compared to an exact Poisson‐normal mixed effects model for counts. The proposed LME model is applied to breast cancer deaths which occurred among white women during 1979–1996. For this example, application of diagnostics for multiparameter likelihood comparisons suggests a restriction of age to a minimum of either 25 or 35, depending on whether small area rate estimates are required. Investigation into a convergence problem led to the discovery that the changes in breast cancer geographic patterns over time are related more to urbanization than to region, as previously thought. Published in 2000 by John Wiley & Sons, Ltd.