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Bias Correction of Nonlinear Effect for Longitudinal Data

Author

Listed:
  • Yuanzhang Li

    (Senior and Primary Statistician, George Washington University, USA)

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.

Suggested Citation

  • Yuanzhang Li, 2018. "Bias Correction of Nonlinear Effect for Longitudinal Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(3), pages 56-60, October.
    • Handle: RePEc:adp:jbboaj:v:8:y:2018:i:3:p:56-60
    DOI: 10.19080/BBOAJ.2018.08.555739
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    References listed on IDEAS

    as
    1. James W. Hardin & Henrik Schmeidiche & Raymond J. Carroll, 2003. "The regression-calibration method for fitting generalized linear models with additive measurement error," Stata Journal, StataCorp LP, vol. 3(4), pages 373-385, December.
    2. James W. Hardin & Raymond J. Carroll, 2003. "Measurement error, GLMs, and notational conventions," Stata Journal, StataCorp LP, vol. 3(4), pages 329-341, December.
    3. James W. Hardin & Henrik Schmeidiche & Raymond J. Carroll, 2003. "The regression-calibration method for fitting generalized linear models with additive measurement error," Stata Journal, StataCorp LP, vol. 3(4), pages 361-372, December.
    Full references (including those not matched with items on IDEAS)

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