The course is strongly use-oriented, stressing practical understanding and interpretation not mathematical derivation. Analysis of covariance, multiple regression, logistic regression, log-linear analysis, and survival analysis. Sample size, power, randomization. Coordination, data management, compliance, interim analysis, and reporting procedures. The student's work should form a coherent whole that can be summarized after 2-3 months of work.
Students have contact with medical investigators as well as supervison by statisticians.
Students present results in oral and written presentations. General linear model-matrix formulation. Estimation, prediction, testing and simultaneous inference. Applications to regression and to analysis of variance and covariance of experimental data. Introduction to a statistical programming environment.