Month Flat Week Day
Annual series held each fall. Emphasis is placed on what clinical and translational researchers collaborating with biostatisticians most need to understand.

Biostatistics Seminar Series: Exploring high-dimensional datasets: Principal Component Analysis (PCA) and clustering

Friday, October 01, 2021 10:30 am - 12:00 pm

Exploratory data analysis is useful to understand a dataset—to find insights and generate hypotheses, rather than test hypotheses. It's easy to understand a dataset when it's small, just a couple of patients and relatively low number of variables. What do you do if there are many variables or many data? How can we understand what's going on in the data, then?

In this session of the TraCS Biostatistics Seminar series, you will learn m...

Biostatistics Seminar Series: Causal inference with observational data: A gentle introduction

Friday, November 05, 2021 10:30 am - 12:00 pm

Biomedical researchers often want to answer causal questions, but they often have access to observational data, not clinical trials. In this session of the TraCS Biostatistics Seminar series, you’ll learn why causal inference is difficult with observational data and what can be done to allow for valid causal inferences if you have observational data.

Presenter: Michael Hudgens, PhD
Professor and Associate Chair, Department of Bio...

Biostatistics Seminar Series: Marginal models and Mixed models for longitudinal outcomes: What’s the difference?

Friday, December 03, 2021 10:30 am - 12:00 pm

Longitudinal data comprise repeated measurements on the same patients taken over time. How should such data be analyzed? Two approaches are common: marginal models and mixed models. These are different kinds of models that yield different interpretations and answer different research questions.

In this session of the TraCS Biostatistics Seminar series, you will learn about the differences as well as the areas of overlap of these stat...

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