Brian L Egleston, PhD
Assistant Research Professor
Office Phone: 215-214-3917
Brian Egleston, PhD received his doctorate in biostatistics from the Johns Hopkins University. He also has a Master of Public Policy from the University of Chicago. His interests include the development of methodology for causal inference, accounting for missing data, and investigating the effects of survey response fatigue. Many of his collaborative analyses have involved the application of state of the art approaches including hierarchical Bayesian, propensity score, competing risk, cost effectiveness, and latent variable methods.Description of research projects
Fox Chase Programs
- 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.
- 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.
- 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. 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. 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. 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. 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. PubMed