Maximum likelihood estimation in generalized linear models with multiple covariates subject to detection limits.
Stat Med
; 30(20): 2551-61, 2011 Sep 10.
Article
em En
| MEDLINE
| ID: mdl-21710558
ABSTRACT
The analysis of data subject to detection limits is becoming increasingly necessary in many environmental and laboratory studies. Covariates subject to detection limits are often left censored because of a measurement device having a minimal lower limit of detection. In this paper, we propose a Monte Carlo version of the expectation-maximization algorithm to handle large number of covariates subject to detection limits in generalized linear models. We model the covariate distribution via a sequence of one-dimensional conditional distributions, and sample the covariate values using an adaptive rejection metropolis algorithm. Parameter estimation is obtained by maximization via the Monte Carlo M-step. This procedure is applied to a real dataset from the National Health and Nutrition Examination Survey, in which values of urinary heavy metals are subject to a limit of detection. Through simulation studies, we show that the proposed approach can lead to a significant reduction in variance for parameter estimates in these models, improving the power of such studies.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Funções Verossimilhança
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Modelos Lineares
/
Limite de Detecção
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
Limite:
Humans
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Male
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article