Dean Billheimer

Dean Billheimer

Professor, Public Health
Director, Statistical Consulting
Professor, Statistics-GIDP
Professor, BIO5 Institute
Member of the General Faculty
Member of the Graduate Faculty
Primary Department
Contact
(520) 626-9902

Work Summary

My research develops new clinical trial and experimental study designs to allow 'learning from data' more efficiently. My research also develops new analysis methods to understand latent structure in data. This allows better understanding of disease processes, better targeting of existing treatments, and development of more effective new treatments. Finally, I am developing new statistical methods based on prediction of future events.

Research Interest

Dean Billheimer, PhD, works with the Arizona Statistics Consulting Laboratory (StatLab) to partner with scientists and physicians to advance discovery and understanding. The 'Stat Lab' provides statistical expertise, personnel and computing resources to facilitate study design and conduct, data acquisition protocols, data analysis, and the preparation of grants and manuscripts. Dr. Billheimer also works to adapt and develop new statistical methods to address emerging problems in science and medicine. Dr. Billheimer facilitates discovery translation and economic development by consulting with public and private organizations external to the University of Arizona. Keywords: Biostatistics, Bioinformatics, Study Design, Bayesian Analysis

Publications

Han, J., Dzierlenga, A. L., Lu, Z., Billheimer, D. D., Torabzadeh, E., Lake, A. D., Li, H., Novak, P., Shipkova, P., Aranibar, N., Robertson, D., Reily, M. D., Lehman-McKeeman, L. D., & Cherrington, N. J. (2017). Metabolomic profiling distinction of human nonalcoholic fatty liver disease progression from a common rat model. Obesity (Silver Spring, Md.), 25(6), 1069-1076.
BIO5 Collaborators
Dean Billheimer, Nathan J Cherrington

Characteristic pathological changes define the progression of steatosis to nonalcoholic steatohepatitis (NASH) and are correlated to metabolic pathways. A common rodent model of NASH is the methionine and choline deficient (MCD) diet. The objective of this study was to perform full metabolomic analyses on liver samples to determine which pathways are altered most pronouncedly in this condition in humans, and to compare these changes to rodent models of nonalcoholic fatty liver disease (NAFLD).

Etzioni, R., Hawley, S., Billheimer, D., True, L. D., & Knudsen, B. (2005). Analyzing patterns of staining in immunohistochemical studies: Application to a study of prostate cancer recurrence. Cancer Epidemiology Biomarkers and Prevention, 14(5), 1040-1046.

PMID: 15894650;Abstract:

Background: Immunohistochemical studies use antibodies to stain tissues with the goal of quantifying protein expression. However, protein expression is often heterogeneous resulting in variable degrees and patterns of staining. This problem is particularly acute in prostate cancer, where tumors are infiltrative and heterogeneous in nature. In this article, we introduce analytic approaches that explicitly consider both the frequency and intensity of tissue staining. Methods: Compositional data analysis is a technique used to analyze vectors of unit-sum proportions, such as those obtained from soil sample studies or species abundance surveys. We summarized specimen staining patterns by the proportion of cells staining at mild, moderate, and intense levels and used compositional data analysis to summarize and compare the resulting staining profiles. Results: In a study of Syndecan-1 staining patterns among 44 localized prostate cancer cases with Gleason score 7 disease, compositional data analysis did not detect a statistically significant difference between the staining patterns in recurrent (n = 22) versus nonrecurrent (n = 22) patients. Results indicated only modest increases in the proportion of cells staining at a moderate intensity in the recurrent group. In contrast, an analysis that compared quantitative scores across groups indicated a (borderline) significant increase in staining in the recurrent group (P = 0.05, t test). Conclusions: Compositional data analysis offers a novel analytic approach for immunohistochemical studies, providing greater insight into differences in staining patterns between groups, but possibly lower statistical power than existing, score-based methods. When appropriate, we recommend conducting a compositional data analysis in addition to a standard score-based analysis. Copyright © 2005 American Association for Cancer Research.

Marri, P., Stern, D., Wright, A., Billheimer, D., & Martinez, F. (2012). Asthma-associated Differences in Microbial Composition of Induced Sputum. J Allergy Clin Immunol.

[Epub ahead of print] PMID: 23265859

Johnson, J. C., Schmidt, C. R., Shrubsole, M. J., Billheimer, D. D., Joshi, P. R., Morrow, J. D., Heslin, M. J., Washington, M. K., Ness, R. M., Zheng, W., Schwartz, D. A., Coffey, R. J., Beauchamp, R. D., & Merchant, N. B. (2006). Urine PGE-M: A metabolite of prostaglandin E2 as a potential biomarker of advanced colorectal neoplasia. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 4(11), 1358-65.

The enzyme cyclooxygenase-2 is expressed in a majority of colorectal carcinomas (CRCs) and is important in prostaglandin production. We have developed an accurate method to measure the urinary metabolite of prostaglandin E(2) (PGE-M) using recently developed mass spectrometric techniques. The purpose of this pre-validation study was to determine if urinary PGE-M levels can be used as a biomarker to discriminate between healthy patients and those with colorectal disease.

Schissler, A. G., Li, Q., Chen, J. L., Kenost, C., Achour, I., Billheimer, D. D., Li, H., Piegorsch, W. W., & Lussier, Y. A. (2016). Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. Bioinformatics (Oxford, England), 32(12), i80-i89.

As 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.