Nicholas Henderson is an Assistant Professor in the Department of Biostatistics. He received his PhD in Statistics from the University of Wisconsin – Madison in 2015. Prior to joining the University of Michigan, he completed a postdoctoral fellowship at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University. His primary research interests include hierarchical modeling, Bayesian nonparametric and machine learning methods, computational statistics,and developing statistical methods for better understanding treatment effect heterogeneity and for supporting individualized medicine.
Research Interests & Projects
Precision medicine, Empirical Bayes methods, Bayesian nonparametrics, Bayesian machine learning methods, Computational statistics, Survival analysis, Data integration
- Henderson NC and Varadhan R. Damped Anderson Acceleration with Restarts and Monotonicity Control for Accelerating EM and EM-like Algorithms. To appear in Journal of Computational and Graphical Statistics.
- Henderson NC, Louis TA, Rosner GL, and Varadhan R. Individualized Treatment Effects with Censored Data via Fully Nonparametric Accelerated Failure Time Models. To appear in Biostatistics.
- Henderson NC and Varadhan R. Bayesian Bivariate Subgroup Analysis for Risk-Benefit Evaluation. Health Services and Outcomes Research Methodology. 2018;18(4), 244-264.
- Henderson NC and Newton MA. Making the Cut: Improved Ranking and Selection for Large-Scale Inference. Journal of the Royal Statistical. Society Series B. 2016; 78, 781-804.
- Henderson NC, Louis TA, Wang C, and Varadhan R. Bayesian Analysis of Heterogeneous Treatment Effects for Patient-Centered Outcomes Research. Health Services and Outcomes Research Methodology. 2016; 16(4): 213-233.
- Lesko CR, Henderson NC, and Varadhan R. Considerations when Assessing Heterogeneity of Treatment Effect in Patient-Centered Outcomes Research. 2018; 100, 22-31.
- Wang C, Louis TA, Henderson NC, Weiss CO, and Varadhan R. BEANZ: A Web-based Software for Bayesian Analysis of Heterogeneous Treatment Effects in Patient-Centered Outcomes Research. Journal of Statistical Software. 2018;85(7), 1-31.
- McDaniel LS, Henderson NC, Rathouz PJ. Fast Pure R Implementation of GEE: Application of the Matrix Package. The R Journal. 2013; 5(1):181-187.