Philip S Boonstra

Philip S Boonstra is an Associate Professor with tenure in the Department of Biostatistics at the University of Michigan's School of Public Health. He holds a PhD (2012) and MS (2009) in Biostatistics from the University of Michigan and a BA in Mathematics and Political Science from Calvin College (2006). His office is located in the School of Public Health Tower, where he maintains an active research program.

Research Focus: Dr. Boonstra specializes in Bayesian methodologies, clinical trial design, and data integration. His work addresses critical public health questions including:

• ECMO efficacy for severe COVID-19 patients
• Safety and efficacy of novel cancer treatments
• Integration of heterogeneous prediction models
• Statistical pedagogy for R programming

He collaborates extensively with the Extracorporeal Life Support Organization (ELSO) and oncology research teams.

Publication Trends: His recent articles demonstrate a strong focus on Bayesian statistical methods applied to clinical challenges. Dominant themes include COVID-19 ECMO outcomes (2021-2024), seamless oncology trial designs (2021), and methodological innovations in Bayesian updating (2020). Cancer research spans lymphoma, prostate cancer, and genetic anticipation in hereditary syndromes.

Academic Contributions: Teaches graduate courses including 'Introduction to R Programming' and 'Design and Analysis of Biostatistics Investigations'. Maintains active software repositories for statistical methodologies and contributes to Cross Validated (2.2k reputation). Current projects involve dose optimization frameworks and shrinkage priors for binary data modeling.