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Difference- and regression-based approaches for bias_for_IRR.pdf (519.51 kB)

Difference- and regression-based approaches for detection of bias

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posted on 2023-11-10, 05:10 authored by Chun Yee LimChun Yee Lim, Corey MarkusCorey Markus, Ronda GreavesRonda Greaves, Tze Ping LohTze Ping Loh

This simulation study was undertaken to assess the statistical performance of six commonly used rejection criteria for bias detection.

The false rejection rate (i.e. rejection in the absence of simulated bias) and the probability of bias detection were assessed for the following: difference in measurements for individual sample pair, the mean of the paired differences, t-statistics (paired t-test), slope < 0.9 or > 1.1, intercept > 50% of the lower limit of measurement range, and coefficient of determination (R2) > 0.95. The linear regressions evaluated were ordinary least squares, weighted least squares and Passing-Bablok regressions. A bias detection rate of < 50% and false rejection rates of >10% are considered unacceptable for the purpose of this study.

Rejection criteria based on regression slope, intercept and paired difference (10%) for individual samples have high false rejection rates and/ or low probability of bias detection. T-statistics (α = 0.05) performed best in low range ratio (lowest-to-highest concentration in measurement range) and low imprecision scenarios. Mean difference (10%) performed better in all other range ratio and imprecision scenarios. Combining mean difference and paired-t test improves the power of bias detection but carries higher false rejection rates.

This study provided objective evidence on commonly used rejection criteria to guide laboratory on the experimental design and statistical assessment for bias detection during method evaluation or reagent lot verification.

History

Journal/Conference/Book title

Clinical Biochemistry

Publication date

2023-04-01

Version

  • Pre-print

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