The SCS tries to offer a number of workshops each year, with some workshops offered every semester. Let us know if you have a suggestion of a workshop that you would like to see offered at UConn!
Workshops on Basic Statistical Methods and Their Interpretations
Exploratory Data Analysis and Visualization in R
- Detecting mistakes and data cleaning.
- Shedding lights on preliminary selection of appropriate analysis methods.
- Exploring relationships among predictors and outcome variables.
This workshop will introduce several useful exploratory data analysis methods and visualization tools in R. Participants can apply these methods and tools using an insurance claim dataset we provide. No experience with coding or the R language is required.
The Power Analysis
A Math-Free Workshop for Power Calculation
Sample size calculation plays an important role in study design and grant proposal preparation. An overview of power analysis will be given. Two case studies will be presented to demonstrate the magic of power calculation for the survival model and the linear mixed effect model using SAS and R.
Outline:
- Basic elements of power analysis
- Design of a survival study based on the Log-Rank test
- The linear mixed effect model: a path for designing a longitudinal study
Multiplicity Adjustment
As the number of hypotheses to be tested grew larger, the Bonferroni correction is too conservative and lacking power. This leads to the introduction of the False Discovery Rate (FDR) which is defined to be the expected proportion of falsely rejected hypotheses out of all rejected hypotheses.
Several modern stepwise methods controlling FDR have been proposed to increase power in the presence of too many hypotheses. We give an overview on the classical and modern multiplicity adjustment methods as well as how to run the procedures in R.
Modern Statistical Methods for Testing Multiple Hypotheses
Outline:
- What is “multiplicity”?
- When does “multiplicity” arise?
- (Example 1) Can dead fish be alive?
- (Example 2) Can one drug cure multiple diseases?
- How can it be handled?
- Procedures (Brace yourself for namedropping!)
- Dead fish revisited
- Drug test revisited
- How else? (Modern method)
- Parallel Gate Keeping
Workshops on the Design of Experiments
Incorporating Statistics into Research Grants
This will be a non-technical session geared towards research scientists who prepare grants and applied statisticians involved in collaborative studies.
Workshops on Specialized Statistical Methods
A Practical Introduction to Structural Equation Modeling in R
Outline:
- Latent variable and the basic elements of an SEM
- Practical demonstration of Factor Analysis in R using lavaan
- Extension of Factor Analysis – Mediation
Missing Data in Surveys
Analysis of Patient-Recorded Outcomes
To be useful to patients, researchers and decision makers, a patient-reported outcome (PRO) must undergo a validation process to support that it measures what it is intended to measure accurately and reliably.
In this workshop, after presentation of some key elements on the development of a PRO measure, the core topics of validity and reliability of a PRO measure will be discussed. Exploratory and confirmatory factor analyses, techniques to understand the underlying structure of a PRO measure, will be described. The topic of mediation modeling will be presented as a way to identify and explain the mechanism that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third variable called the mediator variable.
Also discussed will be item response theory and, time permitting, longitudinal analysis. Illustrations will be provided mainly through real-life and simulated examples.
Workshops on Multivariate Analyses and Dimension Reduction
An Overview of Repeated Measure Analysis
A Practical Introduction to Variable Selection in R
Outline:
- Why use variable selection?
- Stepwise forward and backward regression
- The LASSO method
- The Elastic Net in R
Variable Selection with Demos in R
Including such irrelevant variables leads to unnecessary complexity in the resulting model, making it more difficult to interpret. In this workshop, we will cover two types of variable selection approaches, subset section and shrinkage, which can yield better prediction accuracy and model interpretability.
Various examples with demos in R will be provided to illustrate a more concrete idea of when and how one should apply each method.
Model Selection and Dimension Reduction
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.
Workshops on Data Management, Manipulation, and Visualization
Data Visualizations with R Shiny
Perfect the Imperfect Data – How to Deal with Missing Data in Practice
Outline:
- Types of Missing Data
- Consequence of Missing Data
- Analysis of Missing Data
- Preventing Missing Data
Analysis of Missing Data
Model Selection and Dimension Reduction
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.