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

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.

SUBMITTER: Atem FD

PROVIDER: S-EPMC5319605 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Atem Folefac D FD Qian Jing J Maye Jacqueline E JE Johnson Keith A KA Betensky Rebecca A RA

Journal of the Royal Statistical Society. Series C, Applied statistics 20160627 2

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 imp ...[more]

PMID: 28239197

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