Abstract
Pre-Post intervention factorial design with multiple observations for each subject before and after the intervention frequently arises in Quality-of-Life (QoL) research. Usually not only a global hypothesis on intervention is of interest, but also the hypotheses of pre-post change and also the interaction between intervention and pre-post change. In most practical situations, the distribution of the observed data is unknown and there may exist a number of atypical measurements and outliers. More importantly, QoL outcomes are measured in rating scales and the multiple Quality-of-Life measurements before and after the intervention present complex within-subject correlations. Hence, use of parametric and semi-parametric procedures that impose restrictive distributional and correlation assumptions becomes questionable. This emphasizes the demand for statistical procedures that enable us to accurately and reliably analyze QoL outcomes with minimal conditions. Nonparametric methods offer such a possibility and thus become of particular practical importance in the field of QoL. In this article, we aim to expose researchers and practitioners in the biomedical and behavioral science to nonparametric methods for the analysis of data collected in clustered randomized design. We also illustrate the use of the R package npclust we developed for an easy and user-friendly access to nonparametric methods for the analysis of Quality-of-Life data. It provides procedures for pre-processing the data, performing various hypothesis tests and computing confidence intervals for the estimated effects. The procedures also contain methods for visual display of the results. We illustrate the implemented procedures with Pediatric Asthma Quality-of-Life data collected from the Asthma Randomized Trial of Indoor wood Smoke (ARTIS).