MV - Acceptance Criterias

Min. Samples: The minimum number of eligible samples required in order for the evaluation’s requirements to be fulfilled.

Min. Agreement (%): The minimum percent of N results where the actual measurements are in agreement with their expected values.

Generally it is recommended to set Min. Agreement to 95% because it strikes a balance between clinical significance and statistical reliability in many statistical tests such as the Bland-Altman.

Levels: The number of levels throughout a range that require testing.

Replicates per Level: Number of replicates to be performed at each level or concentration.

Min. Coefficient r²: The minimum measures r-squared value to be considered passing. Values above 0.95 are often deemed acceptable.

Range Extension (%): An added range to for the lowest and highest actual results that will be appended to fulfill the test range. This is for when a test range has a range of 5 to 40, it is very difficult to achieve sample testing at exact 5 and 40.

Example: for a test range of 5 to 40 with a 5% range extension, if the highest measure result is 38 then 1.9 (38 x 0.05) will be contributed to the ranges determined by actual testing but will not exceed the actual test range. For example low and high results of 6 and 36 will achieve a final verified test range of 5 to 37.8.

Error Allowance Type: Sets the Carryover to select a max % difference or Standard Deviation to determine if Carryover exceeds manufacturer claims.

The analyzer’s manufacturer insert for the test should indicate the maximum expected carryover for the test.

Options: Percent or Standard Deviation

Min. Samples Per Range: The minimum number of eligible samples required in order for the Reference Interval to be set. For a verification we recommend minimum 20 samples.

Results Within Range (%): The percent of the population results that must be within the Reference Interval for it to be confirmed. We recommend a 95% to match with statistical confidence intervals for populations.

Error Allowance Type - The type of error (TEa, SEa or REa) that will be used for calculating Error Index agreement between two results. The TEa error values may be set in the test’s settings.

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If TEa values have not been set in the test, only an exact match between two results will yield agreement.

Regression Type - Calculates a correlation value to assess the relationship between two data sets. This value can be set to be greater than or less than a cutoff value in order to determine experiment passing.

Regression Methods Table

Name
Definition
Deming Variance Ratio (vr)

Quantifies the agreement between two measurement methods, considering measurement errors. A maximum allowable difference from 1 is standard. VR=Var(X)+Var(Y)Var(X−Y)VR = \frac{Var(X) + Var(Y)}{Var(X - Y)} Var(X)Var(X) = Variance of X Var(Y)Var(Y) = Variance of Y

Passing Bablok Correlation Coefficient (r)

Measures the strength and direction of the linear relationship between two measurement methods using Passing Bablok regression. A minimum absolute value of 0.95 is standard for strong correlation. r=Cov(X,Y)Var(X)+Var(Y)r = \frac{Cov(X, Y)}{Var(X) + Var(Y)} Cov(X,Y)Cov(X, Y) = Covariance of X and Y Var(X)Var(X) = Variance of X Var(Y)Var(Y) = Variance of Y

Spearman Rank Correlation Coefficient (p)

Measures the strength and direction of a monotonic relationship between two variables. A minimum absolute value of 0.95 is standard for strong correlation. p=1−6∑di2n(n2−1)p = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)} did_i= Difference between ranks of X and Y nn = Number of pairs

Sample Correlation Coefficient (R)

The sample correlation coefficient (R) quantifies the strength and direction of the linear relationship between two variables based on sample data. A min value of 0.95 is standard. R=r2R = \sqrt{r^2} r2r^2 = Coefficient of Determination

Pearson Correlation Coefficient (r)

Measures the strength and direction of a linear relationship between two variables. A minimum absolute value of 0.95 is standard for strong correlation. r=Cov(X,Y)σXσYr = \frac{Cov(X, Y)}{\sigma_X \sigma_Y} Cov(X,Y)Cov(X, Y) = Covariance of X and Y σXσ_X = Standard Deviation of X σYσ_Y = Standard Deviation of Y Cov(X,Y)=1n−1∑i=1n(xi−xˉ)(yi−yˉ)Cov(X, Y) = \frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y}) nn = Number of data points xix_i = Individual data point of variable XyiXy_i = Individual data point of variable Y xˉ\bar{x} = Mean of variable X yˉ\bar{y} = Mean of variable Y

R-squared (R²) Coefficient

R-squared is a statistical measure that represents the proportion of the variance in the dependent variable that is explained by the independent variables in a regression model. It ranges from 0 to 1, with higher values indicating a better fit. Usually >0.95 is considered statistical correlation. R2=1−∑i=1n(yi−y^i)2∑i=1n(yi−yˉ)2R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}i)^2}{\sum{i=1}^{n} (y_i - \bar{y})^2} R2R^2 = Coefficient of determination yiy_i = Observed value y^i\hat{y}_i= Predicted value yˉ\bar{y} = Mean of observed values nn = Sample size