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Title: Nonparametric tests for multivariate data with missing values Authors:  Yue Cui - Missouri State University (United States) [presenting]
Abstract: Quality of Life (QOL) outcomes are important in the management of chronic illnesses. In studies of efficacies of treatments or intervention modalities, QOL scales-multi-dimensional constructs are routinely used as primary endpoints. The standard data analysis strategy computes composite (average) overall and domain scores, and conducts a mixed-model analysis for evaluating efficacy or monitoring medical conditions as if these scores were in continuous metric scale. However, assumptions of parametric models like continuity and homoscedasticity can be violated in many cases. Furthermore, it is even more challenging when there are missing values on some of the variables. We will introduce a purely nonparametric approach in the sense that meaningful and, yet, nonparametric effect size measures are developed. We propose an estimator for the effect size and develop the asymptotic properties. The methods are shown to be, particularly effective in the presence of some form of clustering and/or missing values. Inferential procedures are derived from the asymptotic theory. The Asthma Randomized Trial of Indoor Wood Smoke data will be used to illustrate the applications of the proposed methods. The data was collected from a three-arm randomized trial which evaluated interventions targeting biomass smoke particulate matter from older model residential wood stoves in homes that have kids with asthma.