Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors.
Stat Med
; 27(25): 5217-34, 2008 Nov 10.
Article
em En
| MEDLINE
| ID: mdl-18561290
In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model's goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estudos Epidemiológicos
/
Viés
Tipo de estudo:
Etiology_studies
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Health_economic_evaluation
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
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Male
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Reino Unido