Past Workshops – Spring 2017 Semester

Information about past workshops is available here.

Model Selection and Dimension Reduction: Dealing with High-Dimensional Analysis


In many fields, recent developments have led to an explosion in the number of measurements that can be collected in various settings. While at first exciting, having so many predictors can lead to serious high-dimensional problems. With so many predictors, how does a researcher identify the most important ones? If there are too many predictors (p > n), how can the analysis be carried out? These high-dimension problems that arise from modern data sets have called for a major expansion of the classical statistical toolbox for analyzing data. This workshop will cover the biggest issues in performing high-dimension analysis and will provide the tools needed to be successful; techniques including factor analy- sis to reduce the dimensionality of the predictors (dimension reduction) and LASSO to select the best predictors from those available (model selection), will be covered. These techniques will be presented with corresponding examples and accompanying R scripts that can be done along with the presentation.

Date: Wednesday, May 10, 2017, 09:30AM — 11:30AM

Statistics for Collaborative Research Grants: Strategies for Power Analysis and Statistical Plans


  1. External Grant funding
    – Understanding the RFA
    – Creating a research team
    – Role of statistician
    – Including a statistician on a grant
  2. Pre submission
    – Formulating Specific Aims
    – Focusing Hypotheses
    – PreliminaryData
    – Sample Size and Power
    – Statistical Plans
  3. Post-submission
    – Understanding reviewers comments
    – Responding to a critique
  4. Manuscripts and Publishing
    – Analyses and interpretation
    – Expected work product from a statistician
    – Authorship

Date: Wednesday, May 10, 2017, 01:00PM — 02:30PM

An Overview of Repeated Measure Analysis


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.

Date: Wednesday, May 10, 2017, 03:00PM — 04:30PM