Comput Math Methods Med - Validation study methods for estimating odds ratio in 2 ? 2 ? J tables when exposure is misclassified.

Tópicos

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Resumo

CKGROUND: Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and 2 ? 2 ? J tables.METHODS: Simple estimates for predictive values when misclassification is nondifferential are presented. Using them, we estimated the corrected log odds ratio and its variance for 2 ? 2 ? J tables, using the inverse matrix method. A two-step weighted likelihood method was also developed. Moreover, we compared the matrix and inverse matrix methods to the maximum likelihood (MLE) method using a simulation study.RESULTS: In all situations, the inverse matrix method proved to be more efficient than the matrix method. Matrix and inverse matrix methods for nondifferential situations are more efficient than differential misclassification.CONCLUSIONS: Although MLE is optimal among all of the methods, it is computationally difficult and requires programming. On the other hand, the inverse matrix method with a simple closed-form presents acceptable efficiency.

Resumo Limpo

ckground misclassif exposur variabl epidemiolog studi may lead bias estim paramet loss power statist infer paper invers matrix method effici method correct odd ratio misclassif binari exposur general nondifferenti misclassif j tablesmethod simpl estim predict valu misclassif nondifferenti present use estim correct log odd ratio varianc j tabl use invers matrix method twostep weight likelihood method also develop moreov compar matrix invers matrix method maximum likelihood mle method use simul studyresult situat invers matrix method prove effici matrix method matrix invers matrix method nondifferenti situat effici differenti misclassificationconclus although mle optim among method comput difficult requir program hand invers matrix method simpl closedform present accept effici

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