Current and Past Grad students shine at 2024 ENAR Conference

Check out some of our current and past graduate students who presented research at the 2024 ENAR Conference held March 10-13 in Baltimore, MD

Peter Norwood (PhD 2023) has received the 2024 ENAR Distinguished Student Paper Award! He presented his work, “Adaptive Randomization Methods for Sequential Multiple Assignment Randomized Trials (SMARTs) via Thompson Sampling”.

Siyi Liu 

Title: Multiply Robust Estimators in Longitudinal Studies with Missing Data Under Control-based Imputation

Authors: Siyi Liu, North Carolina State University; Shu Yang, North Carolina State University; Yilong Zhang, Merck & Co., Inc.; Guanghan (Frank) Liu

Abstract: Longitudinal studies are often subject to missing data. The recent guidance from the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario, where the participants in the treated group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves root-n consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a slower rate when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulations and an antidepressant clinical trial.

Hyoshin Kim 

Title:Bayesian Estimation of Clustered Dependence Structures in Functional Neuroconnectivity

Authors: Hyoshin Kim, North Carolina State University; Sujit K Ghosh, North Carolina State University; Emily C Hector, North Carolina State University

Abstract: Motivated by the need to model the dependence between regions of interest in functional neuroconnectivity for efficient inference, we propose a new sampling-based Bayesian clustering approach to estimate structured covariances of high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet process that clusters covariance sub-matrices into independent groups of outcomes, thereby naturally inducing sparsity in the whole brain connectivity matrix. A new split-merge algorithm is employed to achieve convergence of the Markov chain that is shown empirically to recover both uniform and Dirichlet partitions with high accuracy. We investigate the empirical performance of the proposed method through extensive simulations. Finally, the proposed approach is used to group regions of interest into functionally independent groups in the Autism Brain Imaging Data Exchange participants with autism spectrum disorder and attention-deficit/hyperactivity disorder.

Ye Shen 

Title: Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data

Authors: Ye Shen, North Carolina State University,; Rui Song, NCSU; Alberto Abadie, MIT

Abstract: The Synthetic Control method (SC) has become a valuable tool for estimating causal effects. Originally designed for single-treated unit scenarios, it has recently found applications in high-dimensional disaggregated settings with multiple treated units. However, challenges in practical implementation and computational efficiency arise in such scenarios. To tackle these challenges, we propose a novel approach that integrates the Multivariate Square-root Lasso method into the synthetic control framework. We rigorously establish the estimation error bounds for fitting the Synthetic Control weights using Multivariate Square-root Lasso, accommodating high-dimensionality and time series dependencies. Additionally, we quantify the estimation error for the Average Treatment Effect on the Treated (ATT). Through simulation studies, we demonstrate that our method offers superior computational efficiency without compromising estimation accuracy. We apply our method to assess the causal impact of COVID-19 Stay-at-Home Orders on the monthly unemployment rate in the United States at the county level.

 Brandon R Feng 

Title: Somehow, R2D2 Survived: A Variable selection Prior For Survival Modeling

Authors: Brandon R Feng, North Carolina State University; Eric Yanchenko, North Carolina State University; Brian J Reich, North Carolina State University; Ana G Rappold, Environmental Protection Agency

Abstract: The amount of available covariates in medical data is expanding with each passing year, making identification of the most influential factors pivotal in survival regression modeling. Bayesian analyses focusing on variable selection are a common approach towards this problem. However, most use approximations of the posterior to perform this task. In this paper, we propose a placing a beta prior directly on the model coefficient of determination (Bayesian R2), which acts as a shrinkage prior on the global variance of the predictors. Through reparameterization using an auxiliary variable, we are able to update a majority of the parameters with sequential Gibbs sampling, thus reducing reliance on approximate posterior inference and simplifying computation. Performance over competing variable selection priors is then showcased through an extensive simulation study in both censored and non-censored settings. Finally, the method is applied to identifying influential built environment risk factors impacting survival time of Medicare eligible patients in California with cardiovascular ailments.

James Atambire (far right) pictured here speaking during the Fostering Diversity in Biostatistics Workshop panel

James Atambire spoke on Training in Biostatistics Panel at the 2024 Fostering Diversity in Biostatistics Workshop (FDBW).

Yuwen Cheng

Title: Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator

Authors: Yuwen Cheng, North Carolina State University

Abstract: Randomized experiments (REs) are the cornerstone for treatment effect evaluation. However, due to practical considerations, REs may encounter difficulty recruiting sufficient patients. External controls (ECs) can supplement REs to boost estimation efficiency. Yet, there may be incomparability between ECs and concurrent controls (CCs), resulting in misleading treatment effect evaluation. We introduce a novel bias function to measure the difference in the outcome mean functions between ECs and CCs. We show that the ANCOVA model augmented by the bias function for ECs renders a consistent estimator of the average treatment effect, regardless of whether or not the ANCOVA model is correct. To accommodate possibly different structures of the ANCOVA model and the bias function, we propose a double penalty integration estimator (DPIE) with different penalization terms for the two functions. With an appropriate choice of penalty parameters, our DPIE ensures consistency, oracle property, and asymptotic normality even in the presence of model misspecification. DPIE is more efficient than the estimator derived from REs alone, validated through theoretical and experimental results.

Yi Liu

Title: Average Treatment Effect on the Treated, When the Positivity Assumption Is Violated

Authors: Yi Liu, Department of Statistics, North Carolina State University; Huiyue Li, Baim Institute for Clinical Research; Yunji Zhou, Department of Biostatistics, University of Washington; Roland Albert Matsouaka, Department of Biostatistics and Bioinformatics, Duke University

Abstract: The use of propensity score (PS) methods has become ubiquitous in causal inference. At the heart of PS methods is the positivity assumption. Violation to the positivity leads to extreme PS weights when estimating the causal effects. There are a number of methods dealing with the lack of positivity when estimating the average treatment effect (ATE), such as trimming or truncating the extreme estimated PSs, but surprisingly there is no much effort in the same issue for the average treatment effect on the treated (ATT). In this paper, we first review past methods for ATE to deal with lack of positivity, and formalize how to apply them to ATT. We emphasize the underlying intuition behind these methods to better understand their applications and limitations. We further propose a PS weight-based alternative for ATT, called overlap weighted average treatment effect on the treated (OWATT). The appeal of our method lies in its ability to obtain similar or even better results than trimming and truncation while relaxing the constraint to choose a priori a threshold. The method is illustrated via a simulation study and an analysis of racial disparities in health care expenditures.