The problem of measurement error affecting covariates is very common in many scientific areas. Many techniques have been proposed in literature to face this problem. Most of them require several assumptions on the involved variables to be satisfied, otherwise yielding to misleading results. Here, we propose using a flexible parametric model in order to reduce sensitivity to modeling assumptions, mainly on the unobserved and mismeasured phenomenon. The skew normal distribution is used for this purpose. The performance of the method is evaluated through simulation studies, within a case-control setting.
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