My research interests are focused improving how we care for our patients by developing analytics tools that automate providing quantitative and statistical measures to augment qualitative and anecdotal evaluation. This requires technical efforts, to create databases and software, and clinical efforts, to integrate data aggregation, analysis and use into routine processes. Construction of knowledge based clinical practice improvement databases and standardizations in nomenclatures and ontologies needed to automate aggregation for all patients in a practice and enable data exchanges within and among institutions are facets of this work. A recent example includes, design implementation and use of an electronic prescription database to improve per patient treatment plan evaluation and enable longitudinal monitoring of results of practice quality improvement efforts. We are also leading a group, sponsored by our professional societies, to define national standards for naming used in data exchanges for clinical trials. Another facet is improvement of patient treatment plan evaluation. Traditionally qualitative, visual inspection of spatial dose relationships to target and normal tissues is used to evaluate plans. Development of algorithms to calculate vectorized dose volume histograms and other vector based spatial-dose objects provide a means to quantify those evaluations. Recently use of databases of dose information have enabled construction of statistical metrics to improve treatment plan evaluation and development of models for quantifying relationships to outcomes.
Data science applications: data driven clinical practice improvement, multi-institutional analysis of factors affecting patient outcomes and practice characterization, nomenclature and ontology.
Matthew Schipper, PhD, is 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.
Prof. Schipper’s research interests include:
- Use of Biomarkers to Individualize Treatment – Selection of dose for cancer patients treated with Radiation Therapy (RT) must balance the increased efficacy with the increased toxicity associated with higher dose. Historically, a single dose has been selected for a population of patients (e.g. all stage III NSC lung cancer). However, the availability of new biologic markers for toxicity and efficacy allow the possibility of selecting a more personalized dose. I am interested in using statistical models for toxicity and efficacy as a function of RT dose and biomarkers to select an optimal dose for an individual patient. We are studying quantitative methods based on utilities to make this efficacy/toxicity tradeoff explicit and quantitative when biomarkers for one or multiple outcomes are available. We have proposed a simulation based method for studying the likely effects of any model or marker based dose selection on both toxicity and efficacy outcomes for a population of patients. In related projects, we are studying the role of correlation between the sensitivity of a patient’ tumor and normal tissues to radiation. We are also studying how to utilize these techniques in combination with baseline and/or mid-treatment adaptive image guided RT.
- Early Phase Oncology Study Design – An increasingly common feature of phase I designs is the inclusion of 1 or more dose expansion cohorts (DECs) in which the MTD is first estimated using a 3+3 or other Phase I design and then a fixed number (often 10-20 in 1-10 cohorts) of patients are treated at the dose initially estimated to be the MTD. Such an approach has not been studied statistically or compared to alternative designs. We have shown that a CRM design, in which the dose-assignment mechanism is kept active for all patients, more accurately identifies the MTD and protects the safety of trial patients than a similarly sized DEC trial. It also meets the objective of treating 15 or more patients at the final estimated MTD. A follow-up paper evaluating the role of DECs with a focus on efficacy estimation is in press at Annals of Oncology.
Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.
I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data. Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation. I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.
Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:
- Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
- Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
- Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
