Qingxia Chen, Ph.D.
Affiliations: | 2005 | University of North Carolina, Chapel Hill, Chapel Hill, NC |
Area:
Biostatistics Biology, StatisticsGoogle:
"Qingxia Chen"Parents
Sign in to add mentor| Joseph G. Ibrahim | grad student | 2005 | UNC Chapel Hill | |
| (Theory and inference for parametric and semiparametric methods in missing data problems.) | ||||
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Publications
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| Chen Q, Zhang F, Chen MH, et al. (2020) Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study. Lifetime Data Analysis |
| Zhang F, Chen M, Cong XJ, et al. (2020) Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks: Statistical Modelling |
| Diao G, Zeng D, Ibrahim JG, et al. (2017) Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects. Journal of Biopharmaceutical Statistics. 1-12 |
| Li R, Cheng Y, Chen Q, et al. (2016) Quantile association for bivariate survival data. Biometrics |
| Chen Q, Nian H, Zhu Y, et al. (2016) Too many covariates and too few cases? - a comparative study. Statistics in Medicine |
| Chen Q, Zeng D, Ibrahim JG, et al. (2015) Quantifying the average of the time-varying hazard ratio via a class of transformations. Lifetime Data Analysis. 21: 259-79 |
| Zhu H, Ibrahim JG, Chen Q. (2014) Bayesian Case-deletion Model Complexity and Information Criterion. Statistics and Its Interface. 7: 531-542 |
| Chen Q, Ibrahim JG. (2014) A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models. Statistics and Its Interface. 6: 315-324 |
| Chen Q, May RC, Ibrahim JG, et al. (2014) Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. Statistics in Medicine. 33: 4560-76 |
| Chen Q, Wu H, Ware LB, et al. (2014) A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit. International Journal of Statistics in Medical Research. 3: 32-43 |