Multilevel grouped regression for analyzing self-reported health in relation to environmental factors: the model and its application
article
A method for modeling the relationship of polychotomous health ratings with predictors such as area
characteristics, the distance to a source of environmental contamination, or exposure to environmental
pollutants is presented. The model combines elements of grouped regression and multilevel analysis.
The statistical model describes the entire response distribution as a function of the predictors so that
any measure that summarizes this distribution can be calculated from the model. With the model, polychotomous
health ratings can be used, and there is no need for a priori dichotomizing such variables
which would lead to loss of information. It is described how, according to the model, various measures
describing the response distribution are related to the exposure, and the confidence and tolerance intervals
for these relationships are presented. Specific attention is given to the incorporation of random
factors in the model. The application that here serves as an example, concerns annoyance from transportation
noise. Exposure – response relationships obtained with the described method of modeling are
presented for aircraft, road traffic, and railway noise
characteristics, the distance to a source of environmental contamination, or exposure to environmental
pollutants is presented. The model combines elements of grouped regression and multilevel analysis.
The statistical model describes the entire response distribution as a function of the predictors so that
any measure that summarizes this distribution can be calculated from the model. With the model, polychotomous
health ratings can be used, and there is no need for a priori dichotomizing such variables
which would lead to loss of information. It is described how, according to the model, various measures
describing the response distribution are related to the exposure, and the confidence and tolerance intervals
for these relationships are presented. Specific attention is given to the incorporation of random
factors in the model. The application that here serves as an example, concerns annoyance from transportation
noise. Exposure – response relationships obtained with the described method of modeling are
presented for aircraft, road traffic, and railway noise
Topics
Confidence and tolerance intervalsExposure-response relationshipsGrouped regressionPolychotomousRandom effectsSelf-reported healthalgorithmarticlebiological modelbiometrycomputer simulationenvironmental exposureevaluationhealth surveyhumanmathematical computingmental stressmethodologyNetherlandsregression analysisrisk assessmentstatistical analysisstatistical modelstatisticstraffic noiseAlgorithmsBiometryComputer SimulationData Interpretation, StatisticalEnvironmental ExposureHealth Status IndicatorsHealth SurveysHumansModels, BiologicalModels, StatisticalNetherlandsNoise, TransportationNumerical Analysis, Computer-AssistedRegression AnalysisRisk AssessmentStress, Psychological
TNO Identifier
470387
Source
Biometrical Journal, 48(1), pp. 67-82.
Pages
67-82
Files
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