Exploring spatio-temporal patterns of mortality using mixed effects models

Linda Williams Pickle

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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 a change-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.

Original languageEnglish (US)
Pages (from-to)2251-2263
Number of pages13
JournalStatistics in Medicine
Volume19
Issue number17-18
StatePublished - Sep 15 2000

Fingerprint

Mixed Effects Model
Spatio-temporal Patterns
Mortality
Linear Mixed Effects Model
Breast Cancer
Estimate
Overdispersion
Likelihood Inference
Spatial Analysis
Breast Neoplasms
Information Sharing
Change Point
Urbanization
Information Dissemination
Parameter Estimation
Likelihood
Diagnostics
Siméon Denis Poisson
Count
Restriction

All Science Journal Classification (ASJC) codes

  • Epidemiology

Cite this

Pickle, Linda Williams. / Exploring spatio-temporal patterns of mortality using mixed effects models. In: Statistics in Medicine. 2000 ; Vol. 19, No. 17-18. pp. 2251-2263.
@article{5e35d05d88ac4effae536c437cb3d066,
title = "Exploring spatio-temporal patterns of mortality using mixed effects models",
abstract = "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 a change-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.",
author = "Pickle, {Linda Williams}",
year = "2000",
month = "9",
day = "15",
language = "English (US)",
volume = "19",
pages = "2251--2263",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "17-18",

}

Exploring spatio-temporal patterns of mortality using mixed effects models. / Pickle, Linda Williams.

In: Statistics in Medicine, Vol. 19, No. 17-18, 15.09.2000, p. 2251-2263.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Exploring spatio-temporal patterns of mortality using mixed effects models

AU - Pickle, Linda Williams

PY - 2000/9/15

Y1 - 2000/9/15

N2 - 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 a change-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.

AB - 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 a change-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.

UR - http://www.scopus.com/inward/record.url?scp=0034666023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034666023&partnerID=8YFLogxK

M3 - Article

C2 - 10960851

AN - SCOPUS:0034666023

VL - 19

SP - 2251

EP - 2263

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 17-18

ER -