Application of measurement error models to correct for systematic differences among readers and vendors in echocardiography measurements: the CARDIA study

Aisha Betoko, Chike Nwabuo, Bharath Ambale Venkatesh, Erin P. Ricketts, Sejong Bae, Colin Wu, Samuel S. Gidding, Kiang Liu, João A.C. Lima, Christopher Cox

Research output: Contribution to journalArticle


We illustrate the application of linear measurement error models to calibrate echocardiography measurements acquired 20 years apart in the CARDIA study. Of 4242 echocardiograms acquired at Year-5 (1990–1991), 36% were reread 20 years later. Left ventricular (LV) mass and 8 other measurements were assessed. A machine reproducibility study including 96 additional patients also compared Year-5 and Year-25 equipment. A linear measurement error model was developed to calibrate the original Year-5 measurements, incorporating the additional Year-5 reread and machine reproducibility study data, and adjusting for differences among readers and machines. Median (quartiles) of original Year-5 LV mass was 144.4 (117.6, 174.2) g before and 129.9 (103.8, 158.6) g, after calibration. The correlation between original and calibrated LV mass was 0.989 (95% confidence interval: 0.988, 0.990). The original and calibrated measurements had similar distributions. Additional comparisons of original and calibrated data supported the use of the model. We conclude that systematic differences among readers and machines have been accounted for, and that the calibrated Year-5 measurements can be used in future longitudinal comparisons. It is hoped that this paper will encourage the wider application of measurement error models.

Original languageEnglish (US)
Pages (from-to)1315-1324
Number of pages10
JournalJournal of Applied Statistics
Issue number7
StatePublished - May 18 2020



  • Bias
  • calibration
  • echocardiography
  • linear measurement error models
  • systematic differences

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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