Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Alice Liu of the Department of Biostatistics defends her dissertation on "Efficient Estimation of the Quantile Residual Lifetime Using Auxiliary Covariates"
Graduate faculty of the University and all other interested parties are invited to attend.
Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other fatal diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. For experimental designs, such as for randomized control trials, there is often no interest or need to adjust for the confounding factors due to the nature of the randomization. However, when there is missing data, adding the covariate information can help in improving the efficiency of estimators, e.g., the estimated treatment effect. In this study, we proposed an augmented inverse probability weighting estimator (AIPW) for QRL by incorporating the auxiliary covariate information in the presence of right-censoring. Simulation studies shows that our proposed estimator has smaller variance compared to the inverse probability estimator (IPW) and the Kaplan-Meier type estimator for the QRL when the auxiliary covariate is related to the survival outcome. In contrast, there is minimal efficiency gain over our previously proposed test of equality of two QRLs when using the AIPW estimator compared to the IPW or Kaplan-Meier type estimators.
Last Updated On Monday, October 23, 2017 by Valenti, Renee Nerozzi
Created On Monday, June 19, 2017