Brian L. Egleston, MPP, PhD

Brian L. Egleston, MPP, PhD

Clinical Locations

Primary Location

Fox Chase Cancer Center
333 Cottman Avenue
Philadelphia, PA 19111


Associate Research Professor

Biostatistics and Bioinformatics Facility

Member, Molecular Therapeutics

Research Program

Lab Overview

I am an Associate Research Professor and Biostatistician at the Fox Chase Cancer Center. I have had papers published in both the methodological literature (e.g. Journal of the American Statistical Association, Biometrics) and the substantive literature (e.g. New England Journal of Medicine). I am actively involved in a number of behavioral clinical trials, and have experience accounting for missing data in such a context. I am specifically trained in the statistical field of causal inference, and many of my publications are related to methods for the analysis of observational data when confounding by comorbidities or other variables is of concern. I am also involved in pre-clinical biomarker validation and therapeutic research. More recently, I have been investigating the statistical properties of natural language processing algorithms. The mix of research projects allows me to engage in a broad spectrum of cancer and health services research.

Education and Training

Educational Background

  • PhD, Biostatistics, Johns Hopkins University, Department of Biostatistics, Baltimore, Maryland, 2006
  • MPP, Public Policy (with Honors), University of Chicago, Harris School of Public Policy, Chicago, Illinois
  • BA, Political Science and Certificate in Community Health, Tufts University, College of Liberal Arts, Medford, Massachusetts

Honors & Awards

  • Cancer Nursing Writing Award (with Janet Van Cleave, Elizabeth Ercolano, and Ruth McCorkle), 2014
Research Profile

Research Program

Research Interests

  • Development of methodology for causal inference, accounting for missing data and comorbidity summary measures
  • Application of state-of-the-art approaches, including hierarchical Bayesian, propensity score, competing risk, cost effectiveness and latent variable methods
  • Examining the statistical properties of natural language processing algorithms.

Lab Description

  1. I am the statistician on behavioral studies and clinical trials examining the utility of remote counseling for genetics. This is an increasingly important issue as genetic testing becomes more widespread and diffuse. Many physicians may desire that their patients have genetic counseling so that patients can better understand the implications of their test results, both for the patients themselves, and their families. However, many practices cannot afford to hire their own genetic counselors. Remote counseling, either through videoconferencing or telephone, may provide a low cost solution to the genetic counseling needs of both physicians and their patients.
  2. I have increasingly become involved in natural language processing projects. In particular, I am interested in the statistical properties of these algorithms. This is work I have been conducting jointly with Dr. Slobodan Vucetic, a professor at Temple University. I find the work exciting, as the inferential properties (e.g. standard errors) of many techniques have not been well characterized. There is hence ample opportunities for novel and impactful statistical research in this field.
  3. I am involved in biomarker and pre-clinical therapeutic research in a wide range of disease sites. I often use Gamma growth curve models with Generalized Estimating Equations (GEE) to examine dose response effects of drugs in animal models. I am excited to participate in projects that have the potential of resulting in new clinical treatments and etiologic findings.
  4. Comorbidity adjustment is an important goal of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. While many health services researchers have compared the utility of comorbidity scores using data examples, there has been a lack of mathematical rigor in most of the evaluations. We have developed a research program examining the conditions under which individual versus summary measures are most appropriate. We have provided theoretical justification showing that comorbidity scores created analogously to the Charlson Comorbidity Index may be appropriate balancing scores for prognostic modeling and comorbidity adjustment. This work encompasses our general interest in methods for measuring and accounting for comorbidities.
  5. I have collaborated extensively on projects using National Cancer Database (NCDB) and linked SEER-Medicare data. The Surveillance Epidemiology and End Results (SEER) database is maintained by the National Cancer Institute and collects data on tumor characteristics and demographics information about incident cancers. Studies using observational data, such as Medicare claims data, can provide useful information about treatment effectiveness. Of course, well run randomized clinical trials provide the best level of evidence of treatment effects. However, in many fields of medicine, randomized trials have not been feasible in the United States. We have used the SEER-Medicare claims and NCDB data for a number of comparative effectiveness research projects.

Selected Publications

Egleston BL, Bai T, Bleicher RJ, Taylor SJ, Lutz MH, Vucetic S. Statistical inference for natural language processing algorithms with a demonstration using type 2 diabetes prediction from electronic health record notes. Biometrics. 2020 in press doi: 10.1111/biom.13338.

Egleston BL, Pedraza O, Wong YN, Griffin CL, Ross EA, Beck JR. Temporal trends and characteristics of clinical trials for which only one racial or ethnic group is eligible. Contemporary Clinical Trials Communications, 9:135-42, 2018. PMC5898501

Egleston, B.L., Uzzo, R.G., Wong, Y.N. Latent class survival models linked by principal stratification to investigate heterogeneous survival subgroups among individuals with early stage kidney cancer. Journal of the American Statistical Association 2017;112(518):534-546. doi: 10.1080/01621459.2016.1240078. PMID: 28966417; PMCID: PMC5615848

Gilbert EA, Krafty RT, Bleicher RJ, Egleston BL. On the Use of Summary Comorbidity Measures for Prognosis and Survival Treatment Effect Estimation. Health Serv Outcomes Res Methodol. 2017 Dec;17(3-4):237-255. doi: 10.1007/s10742-017-0171-2. Epub 2017 Jun 21. PMID: 29176931; PMCID: PMC5697800.

Bleicher RJ, Ruth K, Sigurdson ER, Beck JR, Ross E, Wong YN, Patel SA, Boraas M, Chang EI, Topham NS, Egleston BL. Time to Surgery and Breast Cancer Survival in the United States. JAMA Oncol. 2016 Mar;2(3):330-9. doi: 10.1001/jamaoncol.2015.4508.

Egleston, B.L., Pedraza, O., Wong,Y.N., Dunbrack, R.L., Jr., Griffin, C.L., Ross, E.A., Beck, J.R. Characteristics of clinical trials that require participants to be fluent in English. Clin Trials.  2015 Dec;12(6):618-26. doi: 10.1177/1740774515592881. PMID: 26152834; PMCID: PMC4643363

Egleston BL, Uzzo RG, Beck JR, Wong Y. A Simple Method for Evaluating Within-Sample Prognostic Balance Achieved by Published Comorbidity Summary Measures. Health Serv Res Health Serv Res. 2015 Aug;50(4):1179-94. doi: 10.1111/1475-6773.12276. PMID: 25523400 PubMed

Austin, S.R., Wong, Y.N., Uzzo, R.G., Beck, J.R., Egleston, B.L. Why summary measures such as the Charlson Comorbidity Index and Elixhauser Score work. Medical Care 2013; in press. PMID: 23703645 PubMed

Egleston BL. Comment on Imai K, Tingley D, Yamamoto T. Experimental designs for identifying causal mechanisms. J. R. Statist. Soc. A 2013; 176(1):35-36.

Egleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Statistics in Medicine 2011. 30(30):3560-72. PMID: 21953305 PubMed

Egleston BL, Cropsey KL, Lazev AB, Heckman CJ. Tutorial on principal stratification-based sensitivity analysis: Application to smoking cessation studies. Clinical Trials. 2010;7(3):286-98. PMID: 20423924 PubMed

Egleston BL, Chandler DW, Dorgan JF. Validity of estimating non-SHBG bound testosterone and estradiol from total hormone measurements in boys and girls. Annals of Clinical Biochemistry. 2010;47(Pt 3):233-41. PMID: 20406780 PubMed

Egleston BL, Dunbrack RL Jr, Hall MJ. Clinical trials that explicitly exclude gay and lesbian patients. New England Journal of Medicine. 2010;362(11):1054-5. PMID: 20237357 PubMed

Egleston BL, Scharfstein DO, MacKenzie E. On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death. Biometrics. 2009;65(2):497-504. PMID: 18759833 PubMed

Egleston BL, Wong YN. Sensitivity analysis to investigate the impact of a missing covariate on survival analyses using cancer registry data. Stat Med. 2009;28(10):1498-511. PMID: 19235263 PubMed


Additional Publications


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