Summary: Repeated measurements of laboratory markers of immunologic or disease status, such as CD4 lymphocyte counts and HIV p24 antigen levels, can be important end points in comparative clinical trials. In this report, we consider comparison of treatment groups with respect to such markers, focusing on a distribution-free approach in which each participant's data are characterized by a single summary statistic. The summary statistics examined are (a) the slope of the least-squares regression of the marker, (b) the average of the last r measurements, and (c) the difference between the averages of the last r and the first s measurements. Under various models of marker time trends, these methods are compared with regard to statistical power. It is found that the slope is usually more efficient than the other two types of summaries. Adaptations for missing data are discussed and illustrated in an analysis of CD4 counts from a recent AIDS clinical trial.
(C) Lippincott-Raven Publishers.