%0 Generic %A Lee, Catherine %A A. Betensky, Rebecca %D 2017 %T Dataset for: Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer’s disease %U https://wiley.figshare.com/articles/dataset/Dataset_for_Time-to-event_data_with_time-varying_biomarkers_measured_only_at_study_entry_with_applications_to_Alzheimer_s_disease/5491672 %R 10.6084/m9.figshare.5491672 %2 https://wiley.figshare.com/ndownloader/files/9497182 %2 https://wiley.figshare.com/ndownloader/files/9497185 %K survival analysis %K Cox model %K time-dependent covariates %K time origin %K delayed entry %K Statistics %K Medicine %X Relating time-varying biomarkers of Alzheimer’s disease (AD) to time-to-event using a Cox model is complicated by the fact that AD biomarkers are sparsely collected, typically only at study entry; this is problematic since Cox regression with time-varying covariates requires observation of the covariate process at all failure times. The analysis might be simplified by using study entry as the time origin and treating the time-varying covariate measured at study entry as a fixed baseline covariate. In this paper, we first derive conditions under which using an incorrect time origin of study entry results in consistent estimation of regression parameters when the time-varying covariate is continuous and fully observed. We then derive conditions under which treating the time-varying covariate as fixed at study entry results in consistent estimation. We provide methods for estimating the regression parameter when a functional form can be assumed for the time-varying biomarker, which is measured only at study entry. We demonstrate our analytical results in a simulation study and apply our methods to data from the Rush Religious Orders Study and Memory and Aging Project, and data from the Alzheimer’s Disease Neuroimaging Initiative. %I Wiley