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Brian L. Egleston, MPP, PhD

Clinical Locations

Primary Location

Fox Chase Cancer Center
333 Cottman Avenue
Philadelphia, PA 19111

About

Associate Research Professor

Member, Molecular Therapeutics

Research Program

Education, Training & Credentials

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 investigation of the effects of survey response fatigue
  • Application of state-of-the-art approaches, including hierarchical Bayesian, propensity score, competing risk, cost effectiveness and latent variable methods

Lab Overview

Recently, we have been examining the conditions under which summary comorbidity scores are valid, including the appropriateness of comorbidity scores to account for clinical prognosis and confounding in observational studies, in collaboration with S.R. Austin, Y.N. Wong, R.G. Uzzo & J.R. Beck.

Comorbidities are co-existing medical conditions that individuals might have in addition to an index condition of interest, such as cancer. 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. In the statistics literature, theoretical justifications have been given for the use of prognostic scores. Heuristically, comorbidity scores are diminutive versions of prognostic scores. They are often added to regressions with other covariates such as age and sex. We examined the conditions under which individual versus summary measures are most appropriate. We expand on other's work, and show that comorbidity scores created analogously to the Charlson Comorbidity Index may be appropriate balancing scores for prognostic modeling and comorbidity adjustment.

Publications

Selected Publications

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 pii: 1740774515592881. [Epub ahead of print] PMID: 26152834 PubMed

 

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.

 

Bleicher RJ, Ruth K, Sigurdson ER, Ross E, Wong YN, Patel SA, Boraas M, Topham NS, Egleston BL. Preoperative delays in the US Medicare population with breast cancer. J. Clin. Oncol. 2012; 30:4485-4492. PMID: 23169513 PubMed

 

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

My NCBI