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BMC Bioinformatics|June 26, 2026
SurvGME: an R package for survival analysis with graphical and measurement error modelsLi-Pang Chen, Grace Y YiStatistical Methods in Medical Research|January 25, 2023
Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confoundersGrace Y Yi, Li-Pang ChenPlos One|September 30, 2024
AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confoundersLi-Pang Chen, Grace Y YiBiometrics|July 21, 2020
Analysis of noisy survival data with graphical proportional hazards measurement error modelsLi-Pang Chen, Grace Y YiPlos One|January 19, 2021
Model-based forecasting for Canadian COVID-19 dataLi-Pang Chen, Qihuang Zhang, Grace Y Yi, et al.Plos One|February 24, 2023
Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccinesQihuang Zhang, Grace Y Yi, Li-Pang Chen, et al.Biostatistics (Oxford, England)|January 18, 2008
A simulation-based marginal method for longitudinal data with dropout and mismeasured covariatesGrace Y YiStatistics in Medicine|January 5, 2019
Weighted causal inference methods with mismeasured covariates and misclassified outcomesDi Shu, Grace Y YiLifetime Data Analysis|September 3, 2015
Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustmentsYing Yan, Grace Y YiBiometrics|March 9, 2022
Zero-inflated Poisson models with measurement error in the responseQihuang Zhang, Grace Y YiPageof 7