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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)2667-2680
Number of pages14
JournalCommunications in Statistics - Theory and Methods
Volume46
Issue number6
DOIs
StatePublished - Mar 19 2017

Keywords

  • Errors in variables
  • linear regression
  • measurement error variance
  • measurement reliability
  • method of moments
  • sensitivity analysis

ASJC Scopus subject areas

  • Statistics and Probability

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