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Assessing the impact of measurement error in modeling change in the absence of auxiliary data

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  • N. David Yanez
  • Ibrahim Aljasser
  • Mose Andre
  • Chengcheng Hu
  • Michal Juraska
  • Thomas Lumley

Abstract

Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error.

Suggested Citation

  • N. David Yanez & Ibrahim Aljasser & Mose Andre & Chengcheng Hu & Michal Juraska & Thomas Lumley, 2017. "Assessing the impact of measurement error in modeling change in the absence of auxiliary data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(6), pages 2667-2680, March.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:6:p:2667-2680
    DOI: 10.1080/03610926.2015.1040508
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