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Elizabeth Handorf, PhD

Elizabeth Handorf, PhD

Associate Professor

Research Program

Lab Overview

My research focuses on statistical analysis of observational data, particularly applied to comparative-effectiveness, cost-effectiveness, and population health. Many important healthcare questions are infeasible or impractical to test via randomized controlled trials. Often, the most rigorous way to evaluate hypotheses in the absence of a randomized trial is by extracting and analyzing information from large observational datasets, such as cancer registries, claims files, electronic medical records (EMR), or census data. I develop innovative methods to analyze such data, often using computationally intensive solutions.

Education and Training

Educational Background

  • PhD, Biostatistics, University of Pennsylvania, Philadelphia, PA, 2012
  • BSE, Systems Engineering, University of Pennsylvania, Philadelphia, PA, 2005


  • International Biometrics Society
  • American Statistical Association
  • Society for Medical Decision Making

Honors & Awards

  • Student Travel Award, International Conference in Health Policy Statistics, 2011
  • Jonathan Raz Award, 2010
  • GlaxoSmithKline Scholar Award, 2010
Research Profile

Research Program

Research Interests

  • Statistical methods for observational data
  • Cost-effectiveness and comparative effectiveness studies
  • Propensity score methods
  • Machine learning methods for prediction and variable selection
  • Assessing sensitivity to unmeasured confounding variables
  • Interventional studies
  • Tissue microarray analysis
  • Tumor growth modeling

Lab Description

  • Collaborative work with clinicians and scientists
    I provide statistical expertise for a wide variety of studies led by Fox Chase Cancer Center investigators, including efficacy analysis of interventions, biomarker analysis and non-therapeutic randomized trials. Often, these studies require advanced statistical analyses or novel applications of existing methods. For example, I developed a novel hierarchical clustering and latent class analysis procedure to evaluate quality of survey responses from an anonymous web panel. (Handorf et al, 2018)
  • Development of sensitivity analysis methods for cost and cost-effectiveness data
    One major drawback of non-randomized data is that groups of patients with different exposures of interest are often systematically different. Although most secondary data allows us to adjust for basic risk factors, such as gender, age and disease characteristics, it is unlikely that all important variables will be available in an observational dataset. These omitted variables, also called unmeasured confounders, may bias conclusions of study results. I developed a comprehensive framework for evaluating the effect of unmeasured confounders of cost and cost-effectiveness outcomes. (Handorf et al, 2013, 2018)
  • Computationally intensive and machine learning methods
    I apply and extend cutting-edge machine learning methods for high-dimensional data.For example, I conducted a comprehensive evaluation of sparse regressions and tree ensemble methods for variable selection when analyzing U.S. Census data.(Handorf 2020) I also use sophisticated ensemble models (e.g. SuperLearner, Bayesian Additive Regression Trees) for estimation of propensity score weights in observational data analysis.
  • Application of methods for observational data to clinically relevant questions
    I have conducted many analyses of large datasets based on questions from clinical collaborators, using methods appropriate for the observational nature of the data and the outcome of interest. For example, I used the National Cancer Database to demonstrate that delays in treatment for head and neck cancers are associated with poorer survival. (Murphy, 2016) I have conducted many studies using data from the SEER registry, linked SEER-Medicare data, and EMR-derived databases.

Selected Publications

Handorf E, Yin Y, Slifker M, Lynch S. Variable selection in social-environmental data: Sparse regression and tree ensemble machine learning approaches. BMC Medical Research Methodology.  2020 Dec 10;20(1):302.

Handorf EA, Heckman CJ, Darlow S, Slifker M, Ritterband L. A hierarchical clustering approach to identify repeated enrollments in web survey data. PLOS One. 2018;13(9):e0204394.

Handorf EA, Bekelman JE, Heitjan DF, Mitra N. Evaluating costs with unmeasured confounding: a sensitivity analysis for the treatment effect. The annals of applied statistics. 2013;7(4):2062-80.

Handorf EA, Heitjan DF, Bekelman JE, Mitra N. Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders. Statistical methods in medical research. 2018;28(7):2227-42.

Ramamurthy C, Handorf EA, Correa AF, Beck JR, Geynisman DM. Cost-effectiveness of abiraterone versus docetaxel in the treatment of metastatic hormone naive prostate cancer. Urologic oncology. 2019;37(10):688-95.

Gabitova-Cornell L, Surumbayeva A, Peri S, Franco-Barraza J, Restifo D, Weitz N, Ogier C, Goldman AR, Hartman TR, Francescone R, Tan Y, Nicolas E, Shah N, Handorf EA, Cai KQ, O'Reilly AM, Sloma I, Chiaverelli R, Moffitt RA, Khazak V, Fang CY, Golemis EA, Cukierman E, Astsaturov I. Cholesterol Pathway Inhibition Induces TGF-β Signaling to Promote Basal Differentiation in Pancreatic Cancer. Cancer Cell. 2020 Oct 12;38(4):567-583.e11.

Lynch SM, Sorice K, Tagai EK, Handorf EA. Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood-wide association study "hits" in black and white men. Cancer. 2020 Jan 1;126(9):1949-1957

Reese JB, Smith KC, Handorf E, Sorice K, Bober SL, Bantug ET, Schwartz S, Porter LS. A randomized pilot trial of a couple-based intervention addressing sexual concerns for breast cancer survivors. Journal of psychosocial oncology. 2019;37(2):242-63.

Fang CY, Ma GX, Handorf EA, Feng Z, Tan Y, Rhee J, Miller SM, Kim C, Koh HS. Addressing multilevel barriers to cervical cancer screening in Korean American women: A randomized trial of a community-based intervention. Cancer. 2017;123(6):1018-26.

Murphy CT, Galloway TJ, Handorf EA, Egleston BL, Wang LS, Mehra R, Flieder DB, Ridge JA. Survival impact of increasing time to treatment initiation for patients with head and neck cancer in the United States. Journal of clinical oncology. 2016;34(2):169-78.

Gerson JN, Handorf E, Villa D, Gerrie AS, et al. Survival outcomes of younger patients with mantle cell lymphoma treated in the rituximab era. Journal of clinical oncology. 2019;37(6):471-80.

Churilla TM, Chowdhury IH, Handorf E, Collette L, Collette S, Dong Y, Alexander BM, Kocher M, Soffietti R, Claus EB, Weiss SE. Comparison of local control of brain metastases with stereotactic radiosurgery vs surgical resection: A secondary analysis of a randomized clinical trial. JAMA oncology. 2019;5(2):243-247.

Joshi SS, Handorf EA, Zibelman M, Plimack ER, Uzzo RG, Kutikov A, Smaldone MC, Geynisman DM. Treatment facility volume and survival in patients with metastatic renal cell carcinoma: A registry-based analysis. European urology. 2018;74(3):387-93.

Wang Y, Bernhardy AJ, Nacson J, Krais JJ, Tan YF, Nicolas E, Radke MR, Handorf E, Llop-Guevara A, Balmaña J, Swisher EM, Serra V, Peri S, Johnson N. BRCA1 intronic Alu elements drive gene rearrangements and PARP inhibitor resistance. Nat Commun. 2019 Dec 11;10(1):5661.

Joshi SS, Handorf E, Strauss D, Correa AF, Kutikov A, Chen DYT, Viterbo R, Greenberg RE, Uzzo RG, Smaldone MC, Geynisman DM. Treatment trends and outcomes for patients with lymph node-positive cancer of the penis. JAMA oncology. 2018;4(5):643-9.

Heckman CJ, Darlow SD, Ritterband LM, Handorf EA, Manne SL. Efficacy of an intervention to alter skin cancer risk behaviors in young adults. American journal of preventive medicine. 2016;51(1):1-11.


Additional Publications


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