Matthew Schipper is an Assistant Professor in the Departments of Radiation Oncology and Biostatistics. He received his Ph.D. in Biostatistics from the University of Michigan in 2006. Prior to joining the Radiation Oncology department he was a Research Investigator in the Department of Radiology at the University of Michigan and a consulting statistician at Innovative Analytics.
Ph.D., Biostatistics, University of Michigan, 2006
M.S., Statistics, Western Michigan University, 2001
B.S., Statistics, Western Michigan University, 2000
Research Interests & Projects
Use of Biomarkers to Individualize and Adapt Treatment Many new markers for radiation related toxicity have been proposed. It is often assumed that the use of these markers in clinical decision making will allow improved outcomes for patients. Motivated by the fact that typical metrics used in evaluating these markers (odds ratios and measures of discriminatory ability such as AUC) do not directly address the ability of a marker to improve efficacy outcomes at a fixed rate of toxicity, I am developing new evaluation metrics for potential dose selection markers. In addition I am working on methods for utilizing models for both toxicity and efficacy to select a patient specific dose of radiation.
Predictive Risk Modeling Prognostic models are commonly used in cancer treatment and research to counsel patients, to select an appropriate treatment and to stratify patients according to risk in clinical trials or other retrospective analyses. I am working with investigators in the Cancer Center to develop and improve such models. For example, we have shown that the CAPRA score, commonly used in prostate cancer, does not adequately account for Gleason pattern 5. We are also investigating how the receipt of hormonal therapy might impact the prognostic ability of CAPRA. Having good risk models is especially crucial when attempting to compare various treatments on the basis of retrospective data.
Early Phase Oncology Study Design Most current phase I trial designs assume that toxicity is binary and also assume that toxicity is fully observed. However, in some cases, it is not possible to ascertain whether a given patient has a dose limiting toxicity (DLT). For instance, brain cancer patients treated with radiation may have imaging findings consistent with either radiation necrosis (toxicity) or tumor progression. I am working on a study design which will extend CRM to accommodate this uncertainty in DLT status. For example, this uncertainty could be captured by the four point ordinal 'degree of relatedness' measure which is typically captured with toxicity reports. Nearly all trials now treat toxicities which are deemed 'possibly related' to study treatment the same as those 'definitely related'.
Search PubMed for publications by Matthew Schipper >>
Schipper M*, Vainshtein J*, Zalupski MM, Lawrence T, Abrams R, Francis IR, Khan G, Leslie W and Ben-Josef E. Prognostic Significance of CA 19-9 in Unresectable Locally Advanced Pancreatic Cancer Treated with Dose-Escalated Intensity Modulated Radiation Therapy and Concurrent Full Dose Gemcitabine: Analysis of a Prospective Phase I/II Dose Escalation Study Int J Radiation Oncology Biology Physics (Accepted). *Contributed equally to this work.
Hunter K, Schipper M, Feng FY, Lyden T, Haxer M, Murdoch-Kinch C, Cornwall B, Lee C, Chepeha D, Eisbruch A. Toxicities affecting Quality of Life After Chemo-IMRT of Oropharyngeal Cancer: Prospective Study of Patient-Reported, Observer-Rated, and Objective Outcomes. Int J Radiation Oncology. Biology Physics (Accepted).
Schipper MJ, Avram A., Kaminski MS and Dewaraja YK. I-131 (2012). Radioimmunotherapy: Prediction of tumor level therapy absorbed dose from the tracer study via a mixed model fit of time-activity. Cancer Biotherapy, 27(7), 403-411.
Sistare F, Goodsaid F, Schipper MJ [29/34], Yu Y. (2010). Towards Establishing Consensus Practices for Qualifying New Safety Biomarkers in Early Drug Development and Regulatory Decision-Making. Nature Biotechnology , May;28(5), 446-54..
Deiterle F, Sistare F, Goodsaid F, Schipper MJ [9/63] (2010). Mattes W. Renal Biomarker Qualification Submission: A dialog between the FDA/EMEA and PSTC. Nature Biotechnology, May;28(5, 455-62.
Schipper MJ, Taylor JM and Lin X. (2008). Generalized monotonic functional mixed models with application to modeling normal tissue complications. Journal of Royal Statistical Society, Series C, 57(2): 1-15.
Schipper MJ, Taylor JM and Lin X. (2007). Bayesian generalized monotonic functional mixed models for the effects of radiation dose histograms on normal tissue complications Statistics in Medicine, 26(25), 4643-56.
- American Statistical Association
- International Biometric Society