Generalizing RCT findings to a Target Population

Research from my PhD dissertation

Recent Publications

More Publications

(2018). Implementing Statistical Methods for Generalizing Randomized Trial Findings to a Target Population. In AddictBeh.

PDF Project

(2018). Measuring model misspecification: application to propensity score methods with complex survey data. In CSDA.


(2017). HIV prevalence and behavioral and psychosocial factors among transgender women and cisgender men who have sex with men in 8 African countries: A cross-sectional analysis. In PLOS Medicine.


Recent & Upcoming Talks

More Talks

Reproducible Data Science: Building a Code Pipeline from End to End
Sep 18, 2018 12:15 PM
Supporting Proactive Diabetes Screenings to Improve Health Outcomes
Aug 15, 2018 5:30 PM
Sensitivity Analysis for an Unobserved Moderator in Trial-to-Target-Population Generalization of Treatment Effects
Mar 1, 2018 10:30 AM

Recent Posts

This is my first time doing 🎉Tidy Tuesday🎉 ! The data for this week came from a FiveThirtyEight blogpost, which breaks down post-college salaries by discipline. The documentation and data for this week can be found in this GitHub repo. One thing I found really interesting in the data was the variable College_jobs, which counted the number of people per major with jobs that required a college degree. I wanted to use this information to look at each major’s median income by percent of recent grads employed in positions requiring/not requiring college degrees.


About three years ago, I received a letter in the mail from Nielsen inviting me to participate in one of their panels. After spending a while on the phone with a representative to determine that it wasn’t a scam, I figured I’d give it a go. I tend to take great interest in knowing where data come from (especially when reporters and media sources try to use statistics to make a point), and as an avid tv watcher, it was cool to learn more about how Nielsen generates ratings and estimates program viewership.