Quantitative Health Sciences Calendar

Abstract:   This presentation will provide an overview of some of her recent work and consists of four parts: 1) a brief introduction to TriNetX data and newly available social determinants of health (SDOH) data; 2) a discussion of the limitations of RWD; 3) an example of RWD use in a causal inference study; and 4) applying machine learning to impute missing data.  The first part will describe the types of RWD available to researchers and will include a sample analysis that uses insights from SDOH data to enhance understanding of the factors contributing to late-stage breast cancer diagnosis. The second part will discuss a research study outlining systemic therapies for breast cancer derived from RWD and will examine the limitations of RWD. The third part will present an initial causal inference analysis designed to determine the effect of GLP-1 administration on the recurrence rate of breast cancer. The final part of the presentation will showcase the application of machine learning to impute missing breast cancer stages. 
Click here to join or call 1 301 715 8592, Meeting ID: 948 2951 6040 password: 202286

Abstract: In developing new treatments (e.g., drugs and biologics) for humans, early development typically employs in vitro and in vivo animal models before advancing to human testing. The transition from non-clinical to clinical testing presents various issues related to doses, safety, and toxicities. Early phase clinical trials, such as Phase Ib (also known as “First-in-Humans”) studies, mark the shift to human subjects and require study designs that test a sequence of doses to identify the maximum tolerated dose, which can then support efficacy testing in subsequent phases. Several study designs are commonly utilized; however, these can fail when new classes of drugs/biologics are being tested. We will review these designs and the complications encountered in the design of a First-in-Humans study for a new regenerative engineered cell therapy to treat emphysema.

Click here to join or call 1 301 715 8592, Meeting ID: 948 2951 6040 password: 202286 in the description box.

For this presentation, please bring your laptop and work alongside. It would be best if you could familiarize yourself with ChatGPT, Consensus: AI, and Grammarly. The free versions of Chat and Census are adequate, although the paid versions are inexpensive and worthwhile. We will use Grammarly for plagiarism checking, and the paid version is required for that functionality.  These are the three main AI programs I use in working for scientific writing. I am excited about our upcoming time together.