Linear regression with a randomly censored covariate: application to an Alzheimer's study

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

Research output: Contribution to journalArticle

Abstract

The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required because of 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 with the simple, but inefficient, complete-case analysis, and with thresholding approaches. We apply the methods to the Alzheimer's study.

Original languageEnglish (US)
Pages (from-to)313-328
Number of pages16
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume66
Issue number2
DOIs
StatePublished - Feb 1 2017

Fingerprint

Linear regression
Covariates
Random Censoring
Dementia
Imputation
Missing Covariates
Positron Emission Tomography
Right Censoring
Censoring
Thresholding
Regression Model
Regression
Imaging
Simulation Study

Keywords

  • Complete case
  • Limit of detection
  • Missing covariate
  • Multiple imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Linear regression with a randomly censored covariate : application to an Alzheimer's study. / Atem, Folefac D.; Qian, Jing; Maye, Jacqueline E.; Johnson, Keith A.; Betensky, Rebecca.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 66, No. 2, 01.02.2017, p. 313-328.

Research output: Contribution to journalArticle

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