Dr. Schipper is a Professor and Director of the division of Biostatistics and Bioinformatics in the Department of Radiation Oncology. He also has a joint appointment as Professor of Biostatistics and serves as an advisor to multiple PhD students in the Biostatistics department. He has developed web apps implementing prediction models and clinical decision aids which have been widely used. He has authored or co-authored more than 170 papers published in peer-reviewed journals.
Areas of Interest
- Predictive risk modeling
- Utility approaches to optimal radiation therapy treatment planning
- Statistical methods for personalized medicine
- Use of biomarkers to individualize and adapt cancer treatment
- Early phase oncology clinical trial design
- PhD, University of Michigan, 2006
R01 CA240991 (PI: Morgan/Spratt)
Determining the clinical impact of gene expression testing in localized prostate cancer
R01 CA233487 (PI: El Naqa)
Optimal Decision Making in Radiotherapy Using Panomics Analytics
R01 EB022075 (PI: Dewaraja)
National Institutes of Health
Imaging and Dosimetry of Yttrium-90 for Personalized Cancer Treatment
F049977 (PI: Schipper)
Rogel Cancer Center Catchment Area Award
Validation of promising toxicity and efficacy biomarkers in Locally Advanced Non-Small Cell Lung Cancer
Role: Principal Investigator
Selected from over 170 publications
- Schipper MJ, Taylor JMG, TenHaken R, Matuzak M, Kong FM and Lawrence T. Personalized dose selection in Radiation Therapy using statistical models for toxicity and efficacy with dose and biomarkers as covariates. Stat Med, 33(30): 5330-9, 2014. PMC4367186. Boonstra PS, Braun TM, Taylor JMG, Kidwell KM, Bellile EL, Daignault S, Zhao L, Griffith KA, Lawrence TS, Kalemkerian GP, Schipper MJ: “Statistical controversies in cancer research: Building the Bridge to Phase II: Efficacy Estimation in Dose Expansion Cohorts.” Annals of Oncology, 28(7):1427-1435, 2017. PMC5834117.
- Soni PD, Hartman HE, Dess RT, ..., Schipper MJ*, Spratt DE*. *Contributed Equally. Comparison of Population-Based Observational Studies with Randomized Trials in Oncology. J Clin Oncol. 2019; 37(14):1209-1216. doi:10.1200/JCO.18.01074. PMC7186578
- Dess RT, Suresh K, Zelefsky MJ, …, Schipper MJ*, Spratt DE*. *Contributed Equally. 2020. Development and Validation of a Clinical Prognostic Stage Group System for Nonmetastatic Prostate Cancer Using Disease-Specific Mortality Results from the International Staging Collaboration for Cancer of the Prostate. JAMA Oncol. 2020 PMID: 33090219; PMC7582232.
- Li P, Taylor JMG, Kong S, Jolly S, Schipper MJ. A utility approach to individualized optimal dose selection using biomarkers. Biom J. 2020 Mar;62(2):386-397. PMID: 31692022; PMCID: PMC9011407.
- Hartman H, Tamura RN, Schipper MJ*, Kidwell KM*. *Contributed Equally as Senior Author. Design and analysis considerations for utilizing a mapping function in a small sample, sequential, multiple assignment, randomized trials with continuous outcomes. Stat Med. First published: 27 October 2020 https://doi.org/10.1002/sim.8776. PMID: 33111381.
- Dess RT, Suresh K, Zelefsky MJ, …, Schipper MJ*, Spratt DE*. *Contributed Equally. 2020. Development and Validation of a Clinical Prognostic Stage Group System for Nonmetastatic Prostate Cancer Using Disease-Specific Mortality Results from the International Staging Collaboration for Cancer of the Prostate. JAMA Oncol. 2020 Oct 22:e204922. doi: 10.1001/jamaoncol.2020.4922. Epub ahead of print. PMID: 33090219; PMCID: PMC7582232.
- Schipper MJ, Yuan Y, Taylor JM, Ten Haken RK, Tsien C, Lawrence TS. A Bayesian dose-finding design for outcomes evaluated with uncertainty. Clinical Trials. 2021;18(3):279-285.
- Li P, Taylor JMG, Boonstra PS, Lawrence TS, Schipper MJ. Utility based approach in individualized optimal dose selection using machine learning methods. Stat Med. 2022 Mar 28. doi: 10.1002/sim.9396. Epub ahead of print. PMID: 35343595.