Title
Stratified Multivariate Mann-Whitney Estimators for the Comparison of Two Treatments with Randomization-Based Covariance Adustment
Speaker
Gary Koch, University of North Carolina
Abstract
Methodology for the comparison of two randomly assigned treatments for strictly ordinal response variables has discussion through multivariate Mann-Whitney estimators with stratification adjustment. Although such estimators can have direct computation as weighted linear combinations of within stratum Mann-Whitney estimators, consistent estimation for their covariance matrix is through methods for multivariate U-statistics. The scope for these methods includes ways of managing randomly missing data and for invoking randomization based covariance adjustment for no differences between treatments for background or baseline covariables. The assessment of treatment differences can be through confidence intervals or statistical tests for the adjusted Mann-Whitney estimators. Three examples have illustrative results presented for the methods in this paper. The first example is a randomized clinical trial with eight strata and a univariate ordinal response variable. The second example is a randomized clinical trial with four strata, two covariables and four ordinal response variables. The third example is a randomized two period crossover clinical trial with four strata, three covariables (as age, screening, first baseline), three response variables (as first period response, second baseline, second period response), and missing data. For these examples, the results are interpretable through the probability of better outcomes for one treatment than the other.
This is joint work with Atsushi Kawaguchi, Biostatistics Center, Kurume University, and Xiaofei Wang, Department of Biostatistics and Bioinformatics, Duke University Medical Center.
