A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits

A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits

Abstract Background In patient-based studies, biomarker data are often subject to left censoring due to the detection limits, or to incomplete sample or data collection. In the context of longitudinal regression analysis, inappropriate handling of these issues could lead to biased parameter estimate...

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Bibliographic Details
Main Authors: MinJae Lee, Mohammad H. Rahbar, Matthew Brown, Lianne Gensler, Michael Weisman, Laura Diekman, John D. Reveille
Format: Article
Language:English
Published: BMC 2018-01-01
Series:BMC Medical Research Methodology
Subjects:
Limit of detection
Left-censoring
Missing early visits
Quantile regression
Multiple imputation
Online Access:http://link.springer.com/article/10.1186/s12874-017-0463-9

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http://link.springer.com/article/10.1186/s12874-017-0463-9

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