Analysis of Multiple Outcomes in Contaminated Trials Reinforced With Validation Data

This paper is concerned with estimation and testing for treatment effects with multivariate outcomes. It primarily focuses on the situation where imperfect diagnostic tools are used to classify subjects into different groups. Oftentimes, there are more expensive and/or invasive diagnostic tools to accurately determine the subjects' status or conditions, yielding partially validated data on a smaller number of subjects. We propose moment-based approaches for estimating and testing treatment effects. We compare our methods with maximum likelihood approach using the EM algorithm, which requires strong assumptions and bears computational burden, and with traditional methods, which ignore the diagnostic tool's imperfection. The proposed methods show advantages in terms of coverage probability, computations efficiency, and robustness. The application of the methods is illustrated with gene-expression data from the Genes-environments & Admixture in Latino Americans (GALA) II study of asthma in Hispanic/Latino children.