Dataset for: Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer’s disease

2017-12-21T03:57:01Z (GMT) by Catherine Lee Rebecca A. Betensky
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.