Jing Qian, of the Department of Biostatistics and Epidemiology at the University of Massachusetts Amherst, will present:
In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer’s amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer’s disease. In this talk, I will describe threshold regression approaches that we developed recently for linear regression models with a covariate that is subject to random censoring. Threshold regression methods allow for immediate testing of the significance of the effect of a censored covariate. In addition, they provide for unbiased estimation of the regression coefficient of the censored covariate. Simulations demonstrate that the proposed estimators have good finite-sample performance, and often offer improved efficiency over existing methods. I will also discuss a principled method for selection of the threshold. The approach is illustrated in application to an Alzheimer’s disease study that investigated brain amyloid levels in older individuals, as a function of maternal age of dementia onset, with adjustment for other covariates.
A social tea will be held at 3:00 p.m. in A434 Mayo. All are Welcome.