Analysts who would like to be introduced to multiple imputation with regression models to handle missing and incomplete data. Biostatisticians, health services and outcomes researchers, and economists who need to study or predict health outcomes or resource utilization. Biostatisticians working in clinical trials who would like to learn about the need for adjusting for covariables in perfectly balanced randomized trials and to be introduced to developing analytic plans for such adjustment. Then a default overall modeling strategy will be described. Then the freely available S-Plus Design library will be overviewed.
Design facilitates most of the steps of the modeling process.
A case study exploring voting tendencies over U.S. counties in the 1992 presidential election will be presented. This short course will survey the advantages of modeling in randomized trials and will provide some guidance in developing a prospective statistical plan for use in a Phase III clinical trial. The methods covered in this course will apply to almost any regression model, including ordinary least squares, logistic regression models, and survival models. He received his Ph.D. in biostatistics from the University of North Carolina, Chapel Hill in 1979, where he studied under P.K. Sen. Dr. Harrell has been involved in statistical computing since 1969 and is the author of many S-Plus functions and SAS procedures. Since 1973 he has been involved in medical applications of statistics, especially in the area of survival analysis and clinical prediction modeling.