5 0 S Abstracts

30A ANALYSIS ON CLINICAL SIGNS AND SYMPTOMS

OF ANTIMICROBIAL TRIALS

Joanna Yang, Robert Francis and Angela Macko Bristol-Myers Squibb Company

Wallingford, Connecticut

In antimicrobial clinical trials, clinical signs and symptoms (S/S) are collected throughout the study period and are evaluated by investigators to assign a categorical overall clinical response for each patient. This rather subjective clinical response is used, in conjunction with bacteriological response, to determine the treatment differences. The vast amount of information from S/S data are neither formally analyzed nor discernibly interpreted.

We are proposing two different approaches to analyze the S/S data: a) unbalanced repeated-measures analysis and b) generalized estimate equation (GEE) method. For the first approach, a composite score which takes into account the different number of S/S from patient to patient will be created at each time point. Methods for linear mixed-effects models with structured covariance matrices will then be applied to these repeated composite scores. The second approach will only consider the S/S at the last evaluation time. Since data far individual S/S are categorical measures, GEE procedures will be used on these individual ratings to compare the treatment differences. Results from these two approaches will be discussed along with that from the traditional overall clinical responses.

31A TRACKING IN RANDOM REGRESSION MIXED MODELS FOR

LONGITUDINAL DATA: GRAPHICAL EXAMINATION AND COMPARISON OF BLUP AND OLS PROCEDURES

Ronald W. Helms and Russell W. Helms University of North Carolina Chapel Hill, North Carolina

In a random regression mixed model (MixMod) for longitudinal data, each subject is assumed to have its own "individual regression," with time as a covariate. Prior to the availability of MixMod ML and REML estimation procedures, such data were often analyzed using an OLS procedure: (1) Fit an OLS regression to the k-th subject's data, producing an estimated intercept, bo~ , and estimated slope(s), bjk; repeat for each k= 1, 2 . . . . K = number of subjects. (2) Treat the bok and bjk as "data" and subject these values to further analyses, typically using general linear univariate or multivariate models. (3) Graph a subject's regression with respect to time (other covariates held constant) as an estimate of the subject's longitudinal "track." A newer procedure is: (1) Fit a MixMod, with random subject effects, to data from all subjects. (2) Compute the Best Linear Unbiased Predictor (BLUP) for each time point (other covariates held constant) for each subject. (3) Graph each subject's BLUPs vs. time as an estimate of the subject's longitudinal "track." This paper comments on the differences between the procedures, their relative advantages and disadvantages, illustrates a method for comparing the procedures graphically, and comments on selecting a procedure.