Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information.
In this manuscript, we present non-parametric two-sample tests for paired censored survival data incorporating longitudinal covariate information. These tests take advantage of information collected at baseline and post-baseline to provide efficiency gains when censoring is uninformative. Additionally, these methods adjust for potential bias from informative censoring that is captured by the baseline and longitudinal covariates. Finite sample properties are investigated with simulation, and we illustrate methodology with an example from the Early Treatment Diabetic Retinopathy Study.
Messinger
Shari
S
Department of Epidemiology and Public Health, University of Miami School of Medicine, 1801 NW 9th Avenue 3rd floor, Miami, FL 33136, USA. smessinger@med.miami.edu
Murray
Susan
S
D003198:Computer Simulation; D003930:Diabetic Retinopathy / Q000628:therapy; D006801:Humans; D008028:Light Coagulation; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016019:Survival Analysis; D014792:Visual Acuity