13 citations · 31 across the 13 of their papers we have counts for
13 papers
Martingale R-learner: Estimating Time-varying Heterogeneous Treatment Effects for Time-to-event Outcomes
Jue Hou, Yuchen Qi, Ronghui Xu
Biological research and clinical evidence suggest that treatment response may vary substantially along characteristics, such as comorbidities, genetic variants, environmental, or s…
Heterogeneous readmission prediction with hierarchical effect decomposition and regularization
Ziren Jiang, Lingfeng Huo, Jue Hou +3
Accurately predicting hospital readmission risks using electronic health records (EHRs) is critical for effective patient management and healthcare resource allocation. Patient pop…
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects
Larry Han, Jue Hou, Kelly Cho +2
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecific…
Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response
Jue Hou, Rajarshi Mukherjee, Tianxi Cai
A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the…
Semi-supervised Approach to Event Time Annotation Using Longitudinal Electronic Health Records
Liang Liang, Jue Hou, Hajime Uno +3
Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to…
Implicit Profiling Estimation for Semiparametric Models with Bundled Parameters
Yucong Lin, Jinhua Su, Yang Liu +2
Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method beco…
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Jue Hou, Zijian Guo, Tianxi Cai
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we d…
Risk Prediction with Imperfect Survival Outcome Information from Electronic Health Records
Stephanie F. Chan, Jue Hou, Xuan Wang +1
Readily available proxies for time of disease onset such as time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor pro…
Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
Jue Hou, Jelena Bradic, Ronghui Xu
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences…
Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference
Jue Hou, Jelena Bradic, Ronghui Xu
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the numb…
High-Dimensional Variable Selection and Prediction under Competing Risks with Application to SEER-Medicare Linked Data
Jiayi Hou, Anthony Paravati, Ronghui Xu +1
Competing risk analysis considers event times due to multiple causes, or of more than one event types. Commonly used regression models for such data include 1) cause-specific hazar…
The Impact of Confounder Selection in Propensity Scores for Rare Events Data - with Applications to Birth Defects
Ronghui Xu, Jue Hou, Christina D. Chambers
Our work was motivated by a recent study on birth defects of infants born to pregnant women exposed to a certain medication for treating chronic diseases. Outcomes such as birth de…
A Nonparametric Maximum Likelihood Approach for Partially Observed Cured Data with Left Truncation and Right-Censoring
Jue Hou, Christina D. Chambers, Ronghui Xu
Partially observed cured data occur in the analysis of spontaneous abortion (SAB) in observational studies in pregnancy. In contrast to the traditional cured data, such data has an…