Projects

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.

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

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(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.

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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.

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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.

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