Work

Linear Regression with a Randomly Censored Covariate: Application to an Alzheimer's Study

Year: 2016

Type: article

Abstract: The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography (PET) imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required due to the random right censoring of the covariate of maternal age of onset of dementia. Prior literature has proposed methods to address the problem of censoring due to assay limit of detection, but not random censoring. We propose imputation methods and a survival regression method that do not require parametric assumptions about the distribution of the censored covariate. Existing imputation methods address missing covariates, but not right censored covariates. In simulation studies, we compare these methods to the simple, but inefficient complete case analysis, and to thresholding approaches. We apply the methods to the Alzheimer's study.

Source: Journal of the Royal Statistical Society Series C (Applied Statistics)

DOI: 10.1111/rssc.12164

Authors: Folefac D. Atem, Jing Qian, Jacqueline E. Maye, Keith A. Johnson, Rebecca A. Betensky

Institutions: Harvard University, University of Massachusetts Amherst, University of Florida, Massachusetts General Hospital

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green