8 citations · 12 across the 7 of their papers we have counts for
8 papers
Joint Modeling of Longitudinal EHR Data with Shared Random Effects for Informative Visiting and Observation Processes
Cheng-Han Yang, Xu Shi, Bhramar Mukherjee
Longitudinal electronic health record (EHR) data offer opportunities to study biomarker trajectories; however, association estimates-the primary inferential target-from standard mo…
A Doubly Robust Framework for Addressing Outcome-Dependent Selection Bias in Multi-Cohort EHR Studies
Ritoban Kundu, Xu Shi, Michael Kleinsasser +3
Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue…
Generating Synthetic Electronic Health Record Data: a Methodological Scoping Review with Benchmarking on Phenotype Data and Open-Source Software
Xingran Chen, Zhenke Wu, Xu Shi +2
We conduct a scoping review of existing approaches for synthetic EHR data generation, and benchmark major methods with proposed open-source software to offer recommendations for pr…
A new statistical approach for joint modeling of longitudinal outcomes measured in electronic health records with clinically informative presence and observation processes
Jiacong Du, Xu Shi, Bhramar Mukherjee
Biobanks with genetics-linked electronic health records (EHR) have opened up opportunities to study associations between genetic, social, or environmental factors and longitudinal…
What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
Stephen Salerno, Emily K. Roberts, Belinda L. Needham +4
In this work, we are motivated by the problem of estimating racial disparities in health outcomes, specifically the average controlled difference (ACD) in telomere length between B…
Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Jiacong Du, Xu Shi, Donglin Zeng +1
Causal inference on the average treatment effect (ATE) using non-probability samples, such as electronic health records (EHR), faces challenges from sample selection bias and high-…
Exploring the big data paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS)
Youqi Yang, Walter Dempsey, Peisong Han +4
Selection bias poses a challenge to statistical inference validity in non-probability surveys. This study compared estimates of the first-dose COVID-19 vaccination rates among Indi…
A Framework for Understanding Selection Bias in Real-World Healthcare Data
Ritoban Kundu, Xu Shi, Jean Morrison +2
Using administrative patient-care data such as Electronic Health Records (EHR) and medical/ pharmaceutical claims for population-based scientific research has become increasingly c…