title: Weighted estimation of the accelerated failure time model in the presence of dependent censoring.
abstract: Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring technique in this data structure. However, neither of these estimators is better than another with respect to efficiency (standard error). In this paper, we propose a new weighted estimator for the accelerated failure time (AFT) model under dependent censoring. One of the advantages in our approach is that these weights are optimal among all the linear combinations of the previously mentioned two estimators. To calculate these weights, a novel resampling-based scheme is employed. Attendant asymptotic statistical results for the estimator are established. In addition, simulation studies, as well as an application to real data, show the gains in efficiency for our estimator.
authors.affiliation: Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, 16802, United States of America.
authors.forename: Youngjoo
authors.initials: Y
authors.lastname: Cho
authors.affiliation: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, 80045, United States of America.
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000821:virology; D000465:Algorithms; D019380:Anti-HIV Agents / Q000008:administration & dosage; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012367:RNA, Viral / Q000097:blood; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016019:Survival Analysis; D013997:Time Factors; D017211:Treatment Failure
chemical_list: D019380:Anti-HIV Agents; D012367:RNA, Viral