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. 2017;66(2):313-328.
doi: 10.1111/rssc.12164. Epub 2016 Jun 27.

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

Folefac D Atem  1 , Jing Qian  2 , Jacqueline E Maye  3 , Keith A Johnson  3 , Rebecca A Betensky  1
Affiliations

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

Folefac D Atem et al. J R Stat Soc Ser C Appl Stat. 2017.

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.

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Figures

Fig. 1
Fig. 1
Kaplan-Meier Estimator of the distribution of mother’s age of dementia onset.
See this image and copyright information in PMC

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