To tackle the Identifiability issue and to Estimate the Misclassification Parameters

Improving Estimation of Misclassification Parameters in Cluster Randomization Studies

Abstract

Cluster randomization studies have become more common in place of traditional trials thatrandomly assign participants one at a time when this method is impractical for theoretical, ethical, orpractical reasons. In the setting of a complementary poison model with potentially misclassified data, weevaluate three interval estimators for binomial misclassification rates: one based on the wald statistic,another on the score statistic, and a third on the profile log-likelihood statistic. As a result of itsimproved power and lower type I error, the redesigned test comes highly recommended. Semiparametrictesting of misclassification estimates Information on the parameters employed in g (x*, z) that underlieparametric models, misclassification, and model and identification-related problems