|
Activity Number:
|
520
- Survival Analysis III
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #327094
|
|
|
Title:
|
Time-To-Event Data with Time-Varying Biomarkers Measured Only at Study Entry, with Applications to Alzheimer's Disease
|
|
Author(s):
|
Catherine Lee* and Rebecca A. Betensky
|
|
Companies:
|
Kaiser Permanente Division of Research and Harvard School of Public Health
|
|
Keywords:
|
Cox model;
time varying covariates;
Alzheimer's disease;
survival analysis
|
|
Abstract:
|
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 two Alzheimer's data sets.
|
Authors who are presenting talks have a * after their name.
