Title: Nonparametric finite mixture: Applications in contaminated trials
Authors: Solomon Harrar - University of Kentucky (United States) [presenting]
Abstract: Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In randomized clinical trials, participants are first classified as having or not having the characteristic of interest by using diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical inference has been recently investigated in parametric model contexts and shown to introduce severe bias in the estimation of treatment effects. The problem is addressed in a fully nonparametric setting. Methods for estimating and testing meaningful yet nonparametric treatment effects are developed. Consistent estimators and asymptotic distributions are provided for the misclassification error rates as well as the treatment effects. The proposed methods are applicable for outcomes measured in ordinal, discrete or continuous scales. The methods do not require any assumptions, such as the existence of moments, on the distribution of the data. Simulation results show significant advantages of the proposed methods in terms of bias reduction, coverage probability and power. The applications of the proposed methods are illustrated with gene expression profiling of bronchial airway brushings in asthmatic and healthy control subjects.