Author: Timothy Moore

Repeated Measures

Repeated measures analysis has been widely used in many fields and care in accounting for the covariance structure is needed when analyzing such data. In this workshop, we present an overview of repeated measures analysis. Specifically, we cover basic concepts of various types of repeated measures data with examples. We also demonstrate sample size calculation for repeated measure data. Finally, a detailed analysis of the repeated measures data from a SCS project is carried out.

Variable Selection

Variable selection, also known as feature screening, is getting much attention in many research areas, especially for large ‘omics data sets. This workshop will introduce why we should do variable selection and some basic variable selection methods including stepwise, forward and backward regression. The Least absolute shrinkage and selection operator (LASSO) method will also be covered as a widely used variable selection method. Furthermore, this workshop will include the elastic net method, which is a combination of the ridge regression and the LASSO method. All the methods will be implemented in R.

Outline:

  1. Why use variable selection?
  2. Stepwise forward and backward regression
  3. The LASSO method
  4. The Elastic Net in R

Presentation Slides: Variable_Selection_Workshop