A computer program for non-parametric analysis of incomplete repeated measures from two samples

Performance of selected nonparametric tests for discrete longitudinal data under different patterns of missing data

Comparison of changes over time of a continuous response variable between treatment groups is often of main interest in clinical trials. When the distributional properties of the continuous response variable are not regular enough, or when the response is discrete, nonparametric techniques have been used. The relative performances of selected repeated measures nonparametric two-sample tests proposed by Wei and Lachin, Koziol, Wei and Johnson, and the adapted Wilcoxon Rank-Sum test are compared through simulations based on quality of life data. The Wilcoxon Rank-Sum test is the most powerful and is not significantly affected by the different patterns of missing data.

Empirical power for distribution-free tests of incomplete longitudinal data with applications to AIDS clinical trials

The design of AIDS clinical trials is of growing importance. These studies tend to be longitudinal and typically involve missing data. HIV-1 RNA is a common endpoint for these studies and is inherently non-normal, although viral load can be measured only within certain bounds, resulting in censored data. We compared several analysis methods, both univariate and multivariate, on the basis of empirical power and provide an illustrative example of data from a controlled clinical trial. Simulated viral load data demonstrate that methods adjusting for baseline data have power increasing with increasing positive intrasubject correlation expected with this type of data. Several summary measures considered have power compatible with multivariate tests.

A computer program for regression analysis of ordered categorical repeated measurements

RMORD is an easy-to-use FORTRAN program for the analysis of clustered ordinal data using the method of Stram, Wei, and Ware. This method constitutes an extension of the proportional-odds model to the situation in which groups of responses are correlated. At each measurement occasion, a proportional-odds regression model is fit to the data by maximizing the occasion-specific likelihood function. The joint asymptotic distribution of the occasion-specific regression parameter estimators is obtained along with a consistent estimator of their asymptotic covariance matrix. RMORD may be used when ordinal measurements are obtained at a common set of observation times for multiple subjects or clusters. Both missing data and covariates which vary within clusters can be accommodated. The program can be run on microcomputers, workstations, and mainframe computers. Two examples illustrating the usage and features of RMORD are provided.

Nonparametric test of stochastic ordering for multiple longitudinal measures

We present a nonparametric approach that tests whether multiple longitudinal measures tend in the same direction over time. It is not required that each measure have the same number of serial observations, or that the observations be evenly spaced. The test and related estimators of group differences are based on the multivariate rank test of Wei and Lachin (1) and multivariate Mann-Whitney shift estimators of Thall and Lachin (2) and Lachin (3). An example is given using a subset of exercise data from a clinical trial of vesnarinone in congestive heart failure.