CE Courses

1. Prediction in Event-Based Clinical Trials (half day),
2. Regression Modeling Strategies (full day)
3. Statistical Machine Learning for Biomedical Data (full day)
4. Introduction to Bayesian Nonparametric Methods for Causal Inference (full day)
5. Joint Modeling of Longitudinal and Time-to-Event Data (full day)
6. Data Science for Statisticians (half day)
7. Health Care Analytics in the Presence of Big Data (half day).

Introductions of the CE courses are listed below:

Prediction in Event-Based Clinical Trials

Instructors: Daniel Heitjan & Gui-shuang Ying

Did you ever wish you could use the accumulating data from your event-based clinical trial to reliably predict its future course? Well, now you can!  Give these instructors a half day at JSM 2018 and they will teach you how - using their Bayesian simulation methods coded in straightforward R. Participants will learn about flexible parametric and nonparametric prediction models for simulating future enrollment and event histories. They will describe applications to real trials, showing how you can predict the timing of future interim analyses, identify efficient enrollment strategies informed by current data, and give DSMBs the best possible information on the likelihood of trial success.  Bring your own computer and data and give their methods a try!

Health Care Analytics in the Presence of Big Data

Instructor: Evan Carey

The phrase "big data" has become widespread, but what does it mean for the practicing healthcare analyst? Come to this course to learn more!  In this course, participants will gain hands-on experience using cutting edge software tools for the analysis of large administrative healthcare datasets, with a focus on Python and Apache Spark. Serial and parallel optimizations techniques using frequentist statistical frameworks and machine learning frameworks will be demonstrated. This course will focus on methods and software rather than the clinical context, but numerous real-world examples will be discussed which will offer a broad  perspective. Students will be provided with a copy of a functioning "virtual machine" with all software and course materials pre-installed.

Regression Modeling Strategies

Instructor: Frank Harrell

When was the last time you had a "statistical modeling tune-up"? How do you keep up to date with methods for developing and validating predictive models, dealing with common analytical challenges, and graphically interpreting regression models?  This course is the answer!  Here is an enlightening and extremely popular course (that's why we offer it nearly every year) which covers multivariable regression modeling strategies, relaxing linearity assumptions, interaction surfaces, differences with machine learning, classification vs. prediction, quantifying predictive accuracy, detailed case studies using R, and more.

Introduction to Bayesian Nonparametric Methods for Causal Inference

Instructors: Jason Roy & Michael Daniels

Have you ever thought about trying more innovative approaches to causal inference, but you didn't know how to begin? Bayesian nonparametric methods (BNP) could be exactly what you are looking for!  In this short course, these experts will review BNP methods and illustrate their use for causal inference in the setting of point treatments, dynamic (longitudinal) treatments, and mediation. The BNP approach to causal inference has several possible advantages over popular semiparametric methods, including efficiency gains, the ease of causal inference on any functionals of the distribution of potential outcomes, the use of prior information, and capturing uncertainty about causal assumption via informative prior distributions. You'll learn even more from their wealth of examples, supported by detailed instructions on software implementation using R.