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Biostatistics and Computational Biology

URMC / Department of Biostatistics and Computational Biology / People & Research / Faculty / Ashkan Ertefaie, PhD

 

Ashkan Ertefaie, PhD

Ashkan ErteFaieAssociate Professor Biostatistics
Associate Professor Neurology
Ph.D. (2011) McGill University

Contact Information

University of Rochester
Dept of Biostatistics and Computational Biology
265 Crittenden Boulevard, CU 420630 
Rochester, New York 14642-0630
 
Office: Saunders Research Building 4205
Phone: (585) 275-2138
Fax: (585) 273-1031
E-mail: Ashkan_Ertefaie@urmc.rochester.edu
Personal Website: https://ertefaie.com

Research Interests

My methodological research interest lies in causal inference, dynamic treatment regimes, sequential multiple assignment randomized trials, comparative effectiveness studies using electronic health records, instrumental variable analyses, high-dimensional data analysis, post selection inference, and survival analysis.

Particularly, I am interested in methodologies that lead to constructing the optimal sequence of treatments in chronic diseases where patients are monitored and treated throughout their life. Thus far, I have been primarily motivated by applications in health services, medicine and criminal justice.

Selected Publications

  • Sun, H., Lu, X., Ertefaie, A., Johnson, B. (2021) Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes, Journal of Causal Inference, in press.

  • Yaeger, J. P., Jones, J., Ertefaie, A., Caserta, M. T., van Wijngaarden, E., & Fiscella, K. (2021). Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants. The Journal of Pediatrics.

  • Ertefaie A, McKay J. R., Oslin D., and Strawderman R. L. (2020). Robust Q-Learning. Journal of the American Statistical Association, to appear.

  • Dasgupta, N, Schwarz, J., Hennessy, S., Ertefaie, A., Dart, R. C. (2019) Causal inference for evaluating prescription opioid abuse using trend-in-trend design. Pharmacoepidemiology and Drug Safety, 28(5), 716-725.

  • Ertefaie, A., and Strawderman, R. L. (2018), Constructing dynamic treatment regimes over indefinite time horizons. Biometrika, 105(4), 963-977.
  • Ertefaie, A., Nguyen, A., Harding, D., Morenoff, J, and Yang, W. (2018), Instrumental Variable Analysis with Censored Data in the Presence of Many Weak Instruments: Application to the Effect of Being Sentenced to Prison on Time to Employment. Annals of Applied Statistics, 12(4), 2647-2673.
  • Artman, W. J.*, Nahum-Shani, I., Wu, T., Mckay, J. R., and Ertefaie, A. (2018). Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime. Biostatistics. https://doi.org/10.1093/biostatistics/kxy064. 
  • Ertefaie, A., Hsu, J., and Small, D. (2018), Discovering Treatment Effect Heterogeneity through Post-treatment Variables with Application to the Effect of Class Size on Math Scores. Journal of the Royal Statistical Society: Series C, 67(4), 917-938.
  • Ertefaie, A., Small, D. S., Ji, X., Leonard, C., and Hennessy, S. (2018). Statistical Power for Trend-in-trend Design. Epidemiology, 29(3), e21-e23.
  • Kaufman, E. J., Ertefaie, A., Small, D. S., Holena, D. N., and Delgado, M. K. (2018). Comparative Effectiveness of Initial Treatment at Trauma Center vs Neurosurgery-Capable Non-Trauma Center for Severe, Isolated Head Injury. Journal of the American College of Surgeons, 226(5), 741-751.
  • Ertefaie, A., Small, D., and Rosenbaum, P. (2017), Quantitative Evaluation of the Trade-off of Strengthened Instruments and Sample Size in Observational Studies. Journal of the American Statistical Association, 113(523), 1122-1134.
  • Ertefaie, A., Asgharian, M. and Stephens, A. D. (2017), Variable Selection in Causal Inference using a Simultaneous Penalization Method. Journal of Causal Inference, 6(1).
  • Shortreed, S., and Ertefaie, A. (2017), Outcome-adaptive Lasso Variable Selection for Causal Inference. Biometrics, 73(4), 1111-1122.
  • Ertefaie, A., Flory, J. H., Hennessy, S., and Small, D. (2017), Instrumental Variable Methods for Continuous Outcomes that Accommodate Non-ignorable Missing Baseline Values. American Journal of Epidemiology, 185(12), 1233-1239.
  • Ertefaie, A., Small, D., Flory, J. H., and Hennessy, S. (2017) A Tutorial on the Use of Instrumental Variables in Pharmacoepidemiology. Pharmacoepidemiology and Drug Safety, 26(4), 357-367.
  • Nahum-Shani, I., Ertefaie, A., Lu, X., Almirall, D., Lynch, K. G., McKay, J. R., and Oslin, D. (2017), A SMART Data Analysis Method for Constructing Adaptive Treatment Strategies in Substance Use Disorders. Addiction, 112(5), 901-909.
  • Ertefaie, A., Small, D., Flory, J. H., Hennessy, S. (2016) A Sensitivity Analysis to Assess Bias Due to Selecting Subjects Based on Treatment Received, Epidemiology, 27, 2, e5-e7.
  • Ertefaie, A., Wu, T., Lynch, K. G., Nahum-Shani, I. (2016) Identifying a Set that Contains the Best Dynamic Treatment Regimes. Biostatistics, 17(1), 135-148.
  • Ertefaie, A., Small, D., Flory, J. H., and Hennessy, S. (2016) Selection Bias when using Instrumental Variable Methods to Compare two Treatments but more than two Treatments are Available. International Journal of Biostatistics, 12,1, 219-232.
  • Ertefaie, A., Shortreed, S., and Chakraborty, B. (2016), Q-learning Residual Analysis: Application to The Effectiveness of Sequences of Antipsychotic Medications for Patients with Schizophrenia. Statistics in Medicine, 35(13), 2221-2234.
  • Ertefaie, A., Asgharian, M. and Stephens, A. D. (2015) Double Bias: Estimation of Causal Effects from Length-Biased Samples in the Presence of Confounding, International Journal of Biostatistics, 11, 1, 69-89.
  • Ertefaie, A., Asgharian, M. and Stephens, A. D. (2014). Propensity Score Estimation in the Presence of Length-biased Sampling: A Non-parametric Adjustment Approach. Stat, 3(1), 83-94.
  • Ertefaie, A. and Stephens, A. D. (2010). Comparing Approaches to Causal Inference for Longitudinal Data: Inverse Probability of Treatment Weighting versus Propensity Scores, International Journal of Biostatistics, 6, 2.