Reducing Selection Bias in Analyzing Longitudinal Health Data with High Mortality Rates

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Published Nov 1, 2010
DOI : https://doi.org/10.56801/10.56801/v9.i.498

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Xian Liu
Uniformed Services University of the Health Sciences, Bethesda MD and Walter Reed National Military Medical Center, Bethesda MD,
Charles C. Engel
Uniformed Services University of the Health Sciences, Bethesda MD and Walter Reed National Military Medical Center, Bethesda MD,
Han Kang
Department of Veterans Affairs
Kristie L. Gore
Walter Reed National Military

Abstract

Two longitudinal regression models, one parametric and one nonparametric, are developed to reduce selection bias when analyzing longitudinal health data with high mortality rates. The parametric mixed model is a two-step linear regression approach, whereas the nonparametric mixed-effects regression model uses a retransformation method to handle random errors across time.

Article Details

Issue
Vol. 9 (2010): Issue 2
Section
Articles