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A measurement variability distribution was developed from our previous work on estimating H-NMR measurement uncertainty for cell culture applications [ 18].
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The distribution-based approach was used including sample variability (distribution) and measurement precision; this approach is used if the needed confidence estimate of MCID is not validated (which is the case for SF-12).
Moreover, the impact of possible measurement variability on model estimate has been performed by using Monte Carlo simulation using normal distribution (Baughman et al. 2010).
Analysis of these data indicates that intra-subject variability is greater than inter-subject variability, whereas measurement variability contributes the least to overall variation.
Measurement variability.
Quantifying measurement variability is, therefore, critical.
This A1C methodology may contribute to greater measurement variability.
Finally, measurement variability was simulated and applied to the data.
The ICC of measurement variability was calculated to be 0.765.
The data analysis involved the following steps Measurement variability.
This may cause an increase in measurement variability and errors.
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