Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We emp...

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Bibliographic Details
Published in:J Appl Stat
Main Authors: Atem, Folefac D, Qian, Jing, Maye, Jacqueline E, Johnson, Keith A, Betensky, Rebecca A
Format: Artigo
Language:Inglês
Published: 2016
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Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC5047523/
https://ncbi.nlm.nih.gov/pubmed/27713593
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/02664763.2016.1155110
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