Statistical Tutorials cost $45 each and you will receive appropriate handouts. There are two choices in each of the concurrent sessions.

Sunday, April 10
8 – 10am

• Power and Sample Size Computations
  Instructor: John Castelloe, SAS

  Power determination and sample size computations are an important aspect of study planning and help produce studies with useful results for minimum resources. This statistical seminar reviews basic methodology for power and sample size computations for a number of analyses including proportion tests, t tests, confidence intervals, equivalence tests, survival analyses, correlation, regression, ANOVA, and more complex linear models. The seminar illustrates these methods with numerous examples using new SAS software: the POWER and GLMPOWER procedures and the PSS web interface. The seminar details how to use the software to compute power and sample size, perform sensitivity analyses for other factors such as variability and type I error rate, and produce customized tables, graphs, and narratives. Some basic understanding of power and sample size computations is assumed.

• Analysis of Survival Data With SAS Software
  Instructor: Gordon Johnston, SAS

  The analysis of lifetime data is an important aspect of statistical work in a variety of fields, including the biomedical and engineering sciences. This seminar provides an overview of the methodology available in the SAS System for the analysis of censored survival data. The seminar illustrates nonparametric methodology, semi-parametric regression models, and fully parametric regression models with numerous examples using the LIFETEST, PHREG, LIFEREG, and RELIABILITY procedures. Basic concepts and methods of survival data analysis the attention in the seminar. The rest of the course focuses on more advanced topics, such as correlated survival data and data from recurrent events. Attendees should have a background in basic statistical methodology, but the seminar provides most of the required background in survival analysis.

10:30am – 12:30pm

• Introduction to Logistic Regression
  Instructor: Bob Derr, SAS

  Logistic regression is one of the basic modeling tools for a statistician or data analyst. This tutorial focuses on the basic methodology behind logistic regression and discusses parameterization, testing goodness of fit, model-building procedures, and model evaluation. The tutorial focuses on the dichotomous response, but direction for handling the ordinal response will also be provided. Attendees should have a solid foundation in regression.

• Using Mixed Models for Repeated Measures and Random Effects Data
  Instructor: Tonya Etchison Balan, SAS

  Data which are clustered or correlated are commonplace in a variety of statistical applications. The general linear mixed model is a useful tool to analyze such data and draw meaningful inferences. We will discuss the mixed model and apply it to prototypical examples. Related topics include the verification of underlying assumptions and the validity of associated statistics. Mixed models are fit with the MIXED procedure, and examples of the use of PROC MIXED are discussed. Attendees should have a solid background in regression and linear models.