Passing-Bablok Regression
Passing-Bablok regression is a nonparametric method-comparison line whose slope is a shifted median of all pairwise slopes, robust to outliers and requiring no distributional assumption about errors.
Passing-Bablok 회귀는 모든 점 쌍 기울기의 이동 중앙값으로 직선을 정하는 비모수 측정법 비교 회귀로, 이상값에 강하고 오차 분포를 가정하지 않습니다.
How do I compare a new laboratory method with a reference method without assuming normal errors?
정규 오차를 가정하지 않고 새 검사법과 기준 검사법을 비교하려면 어떻게 하나요?
Passing-Bablok regression estimates the slope as a shifted median of all pairwise slopes and the intercept as the median of the remaining differences, so a few outliers have little influence. Confidence intervals for slope and intercept test proportional and constant bias, and a CUSUM test checks whether a straight line is adequate.
When to use Passing-Bablok Regression
- Two methods measuring the same quantity are compared on continuous data
- Errors may be non-normal or contain outliers
- The two measurements are highly and positively correlated
When to use something else
- Error variances are known and errors are normal; use Deming regression instead
- You want to describe agreement of individual differences; use a Bland-Altman agreement analysis instead
Assumptions and requirements
- The two measurements are linearly related
- Measurements are continuous and positively correlated
- Error distributions of both methods have similar shape
- Sample size is adequate, ideally 30 or more (at most 4,000 pairs of measurements)
What the analysis reports
- Slope and intercept with confidence intervals
- Whether the slope interval contains 1 and the intercept interval contains 0
- CUSUM linearity test statistic and p value
- Kendall's tau between methods
How to report it
Passing-Bablok regression of [method B] on [method A] (n = [value]) gave a slope of [value], 95% CI [[value], [value]], and an intercept of [value], 95% CI [[value], [value]]; the CUSUM test showed no deviation from linearity (p = [value]).
Related methods
Run Passing-Bablok Regression on Quorant
- Data you need
- a table (CSV or Excel) with an outcome variable and predictor (feature) columns
- Price
- 250 credits for one analysis up to 10,000 rows (complexity level 2); larger or wider data costs more. Optional methodologist review: 1200 credits.
- Checking
- Checked by property tests in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.