Bias Correction of Nonlinear Effect for Longitudinal Data

Yuanzhang Li
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Yuanzhang Li: Senior and Primary Statistician, George Washington University, USA

Biostatistics and Biometrics Open Access Journal, 2018, vol. 8, issue 3, 56-60

Abstract: Studying change from baseline measure in longitudinal data promises to lead to early identify a disease which would benefit both patients and society. The effect of antibody or biomarkers is certainly non-linear for human disease. Using longitudinal data and categorizing the first measurement may help to evaluate the non-linear effect, but it could confound the values of repeated measures and generate bias. We developed modeling strategies for case-control longitudinal studies to estimate and correct the bias. We use this approach in a military dataset to evaluate the association of antibody risk of developing schizophrenia.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:8:y:2018:i:3:p:56-60

DOI: 10.19080/BBOAJ.2018.08.555739

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