The objectives of the Biometrics and Statistical Development Core are first to provide statistical support for all of the projects in the Program, and second to advance the frontiers of methodology available for the analysis of repeated measures and particularly longitudinal data. This research is of major importance in optimizing the analysis of longitudinal data from aged and grafted animals. Both the statistical support roll and the methodological research roll of the Biometrics and Statistical Development Core serve the educational development of graduate students in Biometrics. Statistical support includes assistance in study design and appropriate sample size determination, data base management, summarizing, plotting, and analyzing data, and assistance in writing scientific papers and abstracts. Data analysis often involves estimating and comparing linear and nonlinear models fit to data collected repeatedly over time or dose on the same animals. This requires the use of advanced parametric and nonparametric analysis of variance techniques with which the Statistical Core is familiar, but which are not fully developed. This core's research effort will be focused on two areas. The first is to combine univariate nonlinear random effects models of the type discussed by Lindstrom and Bates (1990) with multivariate linear random effects models introduced by Zucker et al. (1995) in order to derive a multivariate nonlinear random effects model capable of simultaneously estimating, comparing and correlating the growth rates and half lives of several nonlinear growth or decay curves. The second is nonparametric, requiring no distribution assumptions. It is to develop a general linear model for longitudinal growth or time response curves based on randomization which encompasses a broad class of experimental designs and unifies inferences based on randomization as the standard general linear model does for inferences based on normal theory, and to apply this model to the problem of nonparametrically comparing growth curves after adjusting for covariates and possible informative censorship. The research topics posed above are ripe for significant progress and of importance to many of the Center's projects.
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