28 citations · 58 across the 28 of their papers we have counts for
30 papers
The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands
Yi Li, Ashkan Ertefaie, Mark van der Laan
For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well und…
Nonparametric Estimation of Optimal Stochastic Just-In-Time Adaptive Interventions for Distal Outcomes
Jack M. Wolf, Nandita Mitra, Ashkan Ertefaie
Mobile and wearable technologies enable the delivery of just-in-time adaptive interventions (JITAIs) -- interventions that adapt treatment delivery to an individual's rapidly chang…
Optimal Treatment Policy Estimation for Recurrent Events with a Competing Terminal Event: An Instrumented Difference-in-Differences Approach
Ritoban Kundu, James Flory, Sean Hennessy +1
Learning reproducible and generalizable optimal treatment policies for chronic diseases requires large, representative populations with long-term follow-up. Administrative health d…
On Causal Inference for the Survivor Function
Benjamin R. Baer, Ashkan Ertefaie, Robert L. Strawderman
In this expository paper, we consider the problem of causal inference and efficient estimation for the counterfactual survivor function. This problem has previously been considered…
A structural nested rate model for estimating the effects of time-varying exposure on recurrent event outcomes in the presence of death
Daniel Mork, Robert L. Strawderman, Michelle Audirac +2
Assessing the causal effect of time-varying exposures on recurrent event processes is challenging in the presence of a terminating event. Our objective is to estimate both the shor…
Valid post-selection inference for penalized G-estimation
Ajmery Jaman, Ashkan Ertefaie, Michèle Bally +3
Understanding treatment effect heterogeneity is important for decision making in medical and clinical practices, or handling various engineering and marketing challenges. When deal…
Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes
Hao Sun, Ashkan Ertefaie, Luke Duttweiler +1
Real-world clinical decision making is a complex process that involves balancing the risks and benefits of treatments. Quality-adjusted lifetime is a composite outcome that combine…
Adaptive sparsening and smoothing of the treatment model for longitudinal causal inference using outcome-adaptive LASSO and marginal fused LASSO
Mireille E Schnitzer, Denis Talbot, Yan Liu +5
Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not d…
Nonparametric assessment of regimen response curve estimators
Cuong Pham, Benjamin R. Baer, Ashkan Ertefaie
Marginal structural models have been widely used in causal inference to estimate mean outcomes under either a static or a prespecified set of treatment decision rules. This approac…
Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie +4
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect difference…
Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curve
Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson +1
Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a condition…
Causal inference for the expected number of recurrent events in the presence of a terminal event
Benjamin R. Baer, Trang Bui, Daniel Mork +2
While recurrent event analyses have been extensively studied, limited attention has been given to causal inference within the framework of recurrent event analysis. We develop a mu…
Inference for relative sparsity
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated p…
Relative Sparsity for Medical Decision Problems
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension trea…
Structural mean models for instrumented difference-in-differences
Tat-Thang Vo, Ting Ye, Ashkan Ertefaie +5
In the standard difference-in-differences research design, the parallel trends assumption may be violated when the relationship between the exposure trend and the outcome trend is…
Valid post-selection inference in Robust Q-learning
Jeremiah Jones, Ashkan Ertefaie, James R. McKay +2
Q-learning facilitates the development of an optimal adaptive treatment strategy through stagewise regression on a pre-specified set of tailoring variables and confounders. Semipar…
Sensitivity analysis for constructing optimal regimes in the presence of treatment non-compliance
Cuong T. Pham, Kevin G. Lynch, James R. McKay +1
The current body of research on developing optimal treatment strategies often places emphasis on intention-to-treat analyses, which fail to take into account the compliance behavio…
Causal Mediation Analysis: Selection with Asymptotically Valid Inference
Jeremiah Jones, Ashkan Ertefaie, Robert L. Strawderman
Researchers are often interested in learning not only the effect of treatments on outcomes, but also the pathways through which these effects operate. A mediator is a variable that…
A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making
Indrabati Bhattacharya, Brent A. Johnson, William Artman +4
Existing methods in estimating the mean outcome under a given dynamic treatment regime rely on intention-to-treat analyses which estimate the effect of following a certain dynamic…
Instrumented Difference-in-Differences
Ting Ye, Ashkan Ertefaie, James Flory +2
Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variabl…
Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +1
One of the main goals of sequential, multiple assignment, randomized trials (SMART) is to find the most efficacious design embedded dynamic treatment regimes. The analysis method k…
Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +2
The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs…
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
Ashkan Ertefaie, Nima S. Hejazi, Mark J. van der Laan
Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biase…
Robust Q-learning
Ashkan Ertefaie, James R. McKay, David Oslin +1
Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically…