Faculty Bio - Daniel P. Normolle, PhD
Associate Professor of Biostatistics
Director of the Biostatistics Facility at the University of Pittsburgh
Ph.D. in Mathematics (Statistics), State University of New York at Binghamton, 1988
M.A. in Mathematics, State University of New York at Binghamton, 1979
B.S. in Mathematics, Wilkes College, Wilkes-Barre, Pennsylvania, 1977
Design of Dose-Escalation Trials The traditional "cohorts-of-three" dose-escalation trial design is not appropriate for the multi-modality therapies employed in contemporary clinical oncology. It has poor statistical qualities, due to the small number of patients enrolled per dose, and is insufficiently flexible to deal with trial objectives and tolerable levels of toxicity that vary by clinical context. The Department of Radiation Oncology at the University of Michigan was, at my urging, the first in the world to implement the Time-to-Event Continual Reassessment Method (TITE-CRM) paradigm for dose escalation trials (in UMCC 9976), and I have extended the TITE-CRM paradigm in a number of ways, including the use of flexible objective functions, re-estimation on multi-parameter objective functions, integration of Phase II endpoints, use of sub-clinical toxicities in modeling and the use of constrained estimation in modeling. In early 2010, I was awarded, with my co-PI Tom Braun at the University of Michigan, an R01 to study and refine these trial designs. We are now investigating how to efficiently test targeted therapies when patients are also receiving standard chemotherapy.
Biomarker Adaptive Clinical Trials As targeted therapy and individualized medicine come closer to being realized, clinical assessments of the relationships between treatment and biomarkers, and the subsequent effects on downstream biomarkers, increasingly replace clinical toxicity and efficacy as the primary endpoints in clinical trials. Trials initially using dosing derived from animal models, similar but different patients or similar but different treatments acquire biomarker data in real time, allowing data from the most relevant population to supplant less relevant data during the course of the trial. I have designed several trials where, by means of Bayesian modeling, treatment is modulated by data acquired during the trial. Portable designs with well-characterized operating characteristics need to be developed to make these designs widely acceptable.
Statistical Classification I have been analyzing proteomic assays (SELDI-TOF) and (MALDI-TOF) of serum samples from normal subjects and subjects with colon cancer as part of the GLNE EDRN Colon Cancer Proteomics Bakeoff. Mass spectrometry data of this type are characterized by having high dimensionality and significant noise. We believe that valid estimates of sensitivity and specificity of such assays must be estimated from blinded samples not used to train the discriminators. While there are any number of statistical classification and machine learning methods in the literature (e.g., Fisher's Discriminant Analysis, logistic regression, neural networks, support vector machines, penalized discriminant analysis, elastic net, random forests), there is very limited general guidance as to which method is better for any particular type of data, how to choose a method given a data set in hand (when there is only one chance to classify a blinded validation set), and how to allocate a fixed number of samples between a training, validation and testing set. I am currently investigating the questions of method selection and optimal sample allocation to training, validation and testing sets.
Powolny A, Bommareddy A, Normolle D, Beumer J, Nelson J, Singh S. (2011) Phenethyl Isothiocyanate Inhibits Prostate Cancer in TRAMP Mice in Association with Induction of Autophagy and Suppression of Plasma Clusterin. Journal of the National Cancer Institute 103: 571-584. PMID: 21330634.
Zhao L, Morgan m, Parsels L, Maybaum J, Lawrence T, Normolle D. (2011) Bayesian Hierarchical Changepoint Methods in Modeling the Tumor Growth Profiles in Xenograft Experiments. Clinical Cancer Research 17(5): 1057-64. PMID: 21131555.
Normolle DP. (1993) An Algorithm for Robust Nonlinear Analysis of Bioassays. Statistics in Medicine 12(21): 2025-2042. PMID: 8296112.
Lawrence TS, Normolle DP, Davis MA, Maybaum J. (1993) The Use of Biphasic Linear Ramped Pulsed Field Gel Electrophoresis to Quantify DNA Damage Based on Fragment Size Distribution. International Journal of Radiation Oncology Biology and Physics 27(3): 659-663. PMID: 8226161.
Normolle DP, Brown MB. (1994) Identification of Aperiodic Seasonality in Non-Gaussian Time Series. Biometrics 50(3): 798-812. PMID: 7981399.
Dawson LA, Normolle D, Balter JM, McGinn CJ, Lawrence TS, Ten Haken RK. (2002) Analysis of Radiation-Induced Liver Disease using the Lyman NTCP Model. International Journal of Radiation Oncology Biology and Physics 53(4): 810-821. PMID: 12095546.
Muler JH, McGinn CJ, Normolle D, Lawrence T, Brown D, Hejna G, Zalupski MM. (2004) A Phase I Trial Using the Time-to-Event Continual Reassessment Strategy to Escalate Cisplatin combined with Gemacitabine and Radiation Therapy in Pancreatic Cancer. Journal of Clinical Oncology 22(2):238-243. PMID: 14665608.
Normolle D, Lawrence T. (2006) Designing Dose-Escalation Trials With Late-Onset Toxicities Using the Time-to-Event Continual Reassessment Method. Journal of Clinical Oncology 24(27): 4426-4433. PMID: 16983110.
Feng M, Balter JM, Normolle D, Adusumilli S, Cao Y, Chenevert TL, Ben-Josef E. (2009) Characterization of Pancreatic Tumor Motion Using Cine MRI: Surrogates for Tumor Position Should be Used with Caution. International Journal of Radiation Oncology 74(3): 884-91. PMID: 19395190.
Cimprich B, Reuter-Lorenz P, Nelson J, Clark PM, Therrien B, Normolle D, Berman MG, Hayes DF, Noll DC, Peltier S, Welsh RC. (2009) Prechemotherapy Alterations in Brain Function in Women with Breast Cancer. Journal of Clinical and Experimental Neuropsychology Jul 29:1-8. PMID: 19642048.
Normolle D, Pan C, Ben-Josef E, Lawrence T. (2010) An Adaptive Trial of Personalized Radiotherapy for Intrahepatic Cancer. Personalized Medicine 7(2): 197-204.
UPCI Biostatistics Facility
Suite 325 Sterling Plaza
201 N Craig St.
Pittsburgh, PA 15213
Last reviewed: June 30, 2011