Introduction
I am an Associate Professor in the Department of Biostatistics and Computational Biology at the University of Rochester Medical Center.
Ashkan Ertefaie
- Total grant managed: $5.13 million
- Ph.D. (2011) McGill University
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Interests:
Causal Inference
Precision medicine
Semiparametric theory
Machine learning - Dept of Biostatistics and Computational Biology, Rochester, New York
My methodological research interest lies in personalized medicine, sequential multiple assignment randomized trials (SMARTs), causal inference, comparative effectiveness studies using randomized trials and electronic health records, instrumental variable analyses, high-dimensional data analysis, post selection inference, and survival analysis.
During my postdoctoral training at University of Michigan, I was involved with multiple projects on designing and analyzing SMART data and prepared multiple R packages and a SAS macro under supervision of Dr. Susan Murphy.
While continuing to work on personalized medicine, my research at the University of Pennsylvania was mainly focused on analyzing observational data using principal stratification and propensity score methods under supervision of Drs. Dylan Small and Sean Hennessy.
News
- Congratulations to Jeremiah Jones for receiving the 2021 William Jackson Hall Fellowship.
- Welcome to new postdoctoral fellow, Biraj Guha from Texas A & M University. Great to have you join the lab!
- Our manuscript, Estimating mean potential outcome under adaptive treatment length strategies in continuous time, has been accepted for publication in Biometrics.
- Welcome to new postdoctoral fellow, Ben Baer from Cornell University. Great to have you join the lab!
- Our manuscript, Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes, has been accepted for publication in Journal of Causal Inference.
- Our manuscript, Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants, has been accepted for publication in The Journal of Pediatrics. (Click here to read the original news)
- I am exited to start working on our new NINDS R61/R33 grant focused on advancing personalized medicine in PD using harmonized multi-site clinical data.
- Welcome to new postdoctoral fellow, Indrabati Bhattacharya from North Carolina State University. Great to have you join the lab!
- Our manuscript, Robust Q-learning, has been accepted for publication in Journal of the American Statistical Association. (Click here to read the original news)
Preprint
- Jones, J., Ertefaie, A., and Strawderman, R. L. (2022). Valid post-selection inference in Robust Q-learning.arXiv preprint arXiv:2208.03233.
- Pham, C., & Ertefaie, A. (2022). Sensitivity Analysis for Constructing Optimal Treatment Regimes in the Presence of Non-compliance and Two Active Treatment Options.arXiv preprint arXiv:2207.08964.
- Jones, J., Ertefaie, A., & Strawderman, R.L. (2021). Causal Mediation Analysis: Selection with Asymptotically Valid Inference. arXiv:2110.06127.
- Bhattacharya, I., Johnson, B. A., Artman, W., Wilson, A., Lynch, K. G., McKay, J. R., & Ertefaie, A. (2021). A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making. arXiv preprint arXiv:2110.00659.
- Ertefaie, A., Hejazi, N. S., & van der Laan, M. J. (2020). Nonparametric inverse probability weighted estimators based on the highly adaptive lasso. arXiv preprint arXiv:2005.11303.
- Ye, T., Ertefaie, A., Flory, J., Hennessy, S., & Small, D. S. (2020). Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend. arXiv preprint arXiv:2011.03593.
- Artman, W. J., Ertefaie, A., Lynch, K. G., McKay, J. R., & Johnson, B. A. (2020) Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach. arXiv preprint arXiv:2005.10307.
- Artman, W. J., Ertefaie, A., Lynch, K. G., & McKay, J. R. (2020) Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes. arXiv preprint arXiv:2008.02341.
Postdoctoral fellows
Indrabati Bhattacharya
Nonparametric Bayes methods, Q-learning, Partial Compliance.
Biraj Subhra Guha
Nonparametric Bayes methods, Reward Learning, Machine Learning, Partial Compliance.
Benjamin R
Baer
Recurrent event processes, Penalization for sparsity, Algorithmic fairness, Shrinkage estimation.
Start working with Ashkan Ertefaie
Graduate students
William
Artman
Bayesian semiparametric methods, Dynamic treatment regimes, Instrumental variable analyses.
Jeremiah
Jones
Post-selection inference, Precision medicine, Machine learning, Mediation analyses, Causal inference.
Samuel
Weisenthal
Reinforcement learning, Semiparametric theory.
Cuong Pham
Instrumental variable analyses, individualized medicine, Semiparametric theory.
Luke Duttweiler
Individualized medicine, Longitudinal data, Marginal structural models, Semiparametric theory, Causal inference.
Start working with Ashkan Ertefaie
Grants
R61/R33 NS120240 (Ertefaie, McDermott, Venuto)
NINDS awards $3.14 million grant to Drs. Ertefaie, McDermott, and Venuto to advance personalized medicine in Parkinson’s disease using harmonized multi-site clinical data. Parkinson’s disease
R01 DA048764 (Ertefaie)
NIDA awarded $1.57 million grant to Dr. Ertefaie to study the effect of partial treatment compliance in constructing individualized treatment strategies. The grant aims to
R21AA027571 (Ertefaie)
NIAAA awarded $0.42 million grant to Dr. Ertefaie to develop individualized treatment strategies for controlling alcohol use. Our overarching aim is to address the need
Featured publications
Robust Q-learning
Ertefaie A, McKay J. R., Oslin D., and Strawderman R. L. (2020). Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1753522 Q-learning is a regression-based approach that is
Constructing dynamic treatment regimes over indefinite time horizons
Ertefaie, A., and Strawderman, R. L. (2018). Biometrika, 105(4), 963-977. Existing methods for estimating optimal dynamic treatment regimes are limited to cases where a utility function is
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
Ertefaie, A., Nguyen, A., Harding, D., Morenoff, J, and Yang, W. (2018). Annals of Applied Statistics, 12(4), 2647-2673. This article discusses an instrumental variable approach for analyzing
Quantitative Evaluation of the Trade-off of Strengthened Instruments and Sample Size in Observational Studies
Ertefaie, A., Small, D., and Rosenbaum, P. (2017). Journal of the American Statistical Association, 113(523), 1122-1134.
Outcome-adaptive lasso: Variable selection for causal inference
Shortreed, S., and Ertefaie, A. (2017), Biometrics, 73(4), 1111-1122. Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a
Selected publications
- Ertefaie, A., Hejazi, N. S., and van der Laan, M. J. (2022). Nonparametric inverse probability weighted estimators based on the highly adaptive lasso. Biometrics.
- Ye, T., Ertefaie, A., Flory, J., Hennessy, S., & Small, D. S. (2022). Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend. Biometrics, accepted with discussion.
- Zhao, Q., Small, D. S., and Ertefaie, A. (2022) Selective inference for effect modification via the lasso, Journal of the Royal Statistical Society: Series B. (Accepted)
- Jones*, J., Ertefaie, A., & Shortreed, S. (2022) Rejoinder to “Outcome-adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017), Biometrics.
- Pisani, T., et. al. (2022) Individuals Who Text Crisis Text Line: Key Characteristics and Opportunities for Suicide Prevention. Suicide and Life-Threatening Behavior.
- Gould, M., et. al. (2022) Crisis Text Line Interventions: Evaluation of Texters’ Perceptions of Effectiveness. Suicide and Life-Threatening Behavior.
- Yaeger, J. P., Lu, J., Jones, J., Ertefaie, A., Fiscella, K., and Gildea, D. (2022) Derivation of a natural language processing algorithm to identify febrile infants. Journal of Hospital Medicine.
- doi:10.1002/jhm.12956
- Artman, W. J., Ertefaie, A., Lynch, K. G., McKay, J. R. and Johnson, B. A. (2022) A Marginal Structural Model for Partial Compliance in SMARTs. Annals of Applied Statistics.
- Artman, W. J., Ertefaie, A., Lynch, K. G., & McKay, J. R. (2022) Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes. Statistics in Medicine.
- Sun, H., Ertefaie, A, and Johnson, B. A. (2021) Estimating mean potential outcome under adaptive treatment length strategies in continuous time. Biometrics.
- Sun, H., Lu, X., Ertefaie, A., Johnson, B, A. (2021) Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes, Journal of Causal Inference, 8(1), 300-314.
- Ertefaie, A., McKay, J. R., Oslin, D., & Strawderman, R. L. (2021). Robust Q-learning. Journal of the American Statistical Association. 116(533), 368-381.
- 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.
- 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.
