Model uncertainty and risk estimation for experimental studies of quantal responses.
Risk Anal
; 25(2): 291-9, 2005 Apr.
Article
em En
| MEDLINE
| ID: mdl-15876205
Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.
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Base de dados:
MEDLINE
Assunto principal:
Exposição Ocupacional
/
Medição de Risco
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article