Hi, I’m Paul. First and foremost, I am on a mission to help advance health and medicine. Growing up with cystic fibrosis (CF), I was exposed early to the world of clinical trials and research pipelines for new medications and devices. Few areas show the payoff of that research better than CF. Over the course of my lifetime I’ve watched the outlook go from patients barely reaching adulthood to many living into middle age and beyond. Wanting to help drive that kind of progress in other conditions, I have worked in clinical data science over a spectrum of clinical domains in varying capacities (research, consulting, platform technology). I am currently working on independent analyses related to better understanding and predicting clinical trial results and corresponding regulatory decisions.

An academic interest of mine is the language and statistics of causation. Does A cause B? With what strength? Through what path? My PhD work explored a sub-field of causal inference known as quantitative bias analysis. Bias analysis asks how much a study’s estimate would change under explicit, quantified assumptions about its biases. I found this area particularly attractive because it allows for greater transparency in epidemiological studies. It moves the conversation past speculation about bias, and gives a more realistic picture of uncertainty by combining random and systematic error.

In the world of programming, I fell in love with the idea of open-source R & Python packages that can be downloaded and used by anyone in the world. I made it my mission during my PhD to incorporate package development work into my research. The result of this was a tool called multibias that allows for the simultaneous adjustment of multiple biases in causal inference. A few years later, I gave it a fresh coat of paint and finally got the package accepted into CRAN (The Comprehensive R Archive Network). I am continually maintaining and promoting this package with the goal of getting more mainstream adoption of bias analysis.

Throughout this journey, I’ve maintained a passion for baseball analytics. I have played competitive fantasy baseball for over 15 years and thrive in the competitive, analytical environment. During school I always chose baseball as the subject for biostatistics class projects. I’ve also contributed a bit to Pitcher List, which has a fun and welcoming community. I continually return to baseball data as an outlet to explore and learn new data science concepts. If this work happens to create insights that help my fantasy baseball team - that’s a win-win!

Publications