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Related Experiment Videos

Does data rounding-off influence reproducibility index estimates?

Bruno Giraudeau1, Philippe Ravaud, Jean Yves Mary

  • 1INSERM CIC 202, Faculte de Medecine, 2bis Bd Tonnelle, 37032 Tours cedex, France. giraudeau@med.univ-tours.fr

Journal of Applied Measurement
|February 11, 2005
PubMed
Summary
This summary is machine-generated.

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Rounding continuous data to a finite precision has minimal impact on the intraclass correlation coefficient (ICC) reproducibility estimates. Practical consequences on ICC estimates can generally be ignored if rounding error is less than 0.3 times the data

Area of Science:

  • Biostatistics
  • Measurement Science
  • Data Analysis

Background:

  • The intraclass correlation coefficient (ICC) is a key metric for assessing the reproducibility of continuous data.
  • Continuous data often possess finite precision due to rounding-off errors.
  • The impact of this finite precision on ICC estimates is not always clear.

Purpose of the Study:

  • To investigate the effect of finite data precision on intraclass correlation coefficient (ICC) estimates.
  • To compare ICC estimates derived from rounded data versus data with infinite precision.

Main Methods:

  • Comparison of ICC estimates between datasets with finite precision (rounded) and infinite precision.
  • Analysis focused on the relationship between the level of rounding-off error and the resulting ICC estimate.

Related Experiment Videos

Main Results:

  • Rounding-off data has no practical consequence on the ICC estimate when the rounding level is less than 0.3 times the standard deviation of the outcome (0.3sigmaX).
  • This threshold suggests that minor data rounding is unlikely to significantly affect reproducibility assessments.

Conclusions:

  • In most practical scenarios, the impact of data rounding on the estimation of reproducibility levels (ICC) can be disregarded.
  • Researchers can be confident in using rounded continuous data for ICC calculations under typical precision levels.