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Author ORCID Identifier
N/A
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Mathematics
Year Degree Awarded
2018
Month Degree Awarded
September
First Advisor
Jing Qian
Subject Categories
Biostatistics | Statistical Models | Survival Analysis
Abstract
Delayed entry arises frequently in follow-up studies for survival outcomes, where additional study subjects enter during the study period. We propose a quantile regression model to analyze survival data subject to delayed entry and right-censoring. Such a model offers flexibility in assessing covariate effects on survival outcome and the regression coefficients are interpretable as direct effects on the event time. Under the conditional independent censoring assumption, we proposed a weighted martingale-based estimating equation, and formulated the solution finding as a $\ell_1$-type convex optimization problem, which was solved through a linear programming algorithm. We established uniform consistency and weak convergence of the resultant estimators. We developed and justified a resampling inference procedure for variance and covariance estimation. The finite-sample performance of the proposed method was demonstrated via simulation studies. The proposed method was illustrated through an application to an atomic bomb survivors study.
DOI
https://doi.org/10.7275/12760256
Recommended Citation
Sun, Boqin, "QUANTILE REGRESSION FOR SURVIVAL DATA WITH DELAYED ENTRY" (2018). Doctoral Dissertations. 1457.
https://doi.org/10.7275/12760256
https://scholarworks.umass.edu/dissertations_2/1457