Statistical method validation for test laboratories
An intensive three-day course on statistical method validation for test laboratories
| What | accreditation LIMS LIS Workshop ISO 17025 laboratory |
|---|---|
| When |
2012-02-28 09:00
to 2012-03-02 17:00 |
| Where | Emperor’s Palace, Johannesburg |
| Contact Name | Application Peak Training & Projects |
| Contact Email | applicationpeaktraining@neomail.co.za |
| Contact Phone | +27 11 402 0110 |
| Add event to calendar |
|
Method validation is the process that provides evidence that a given analytical method, when correctly applied produces results that are fit for purpose. No matter how well a method performs elsewhere, analysts need to confirm that the method is valid when applied in their laboratory. There is now much greater emphasis on method validation in the ISO 17025 accreditation standards
The performance of analytical method is primarily measured around precision and accuracy. Laboratories are required to validate the methods they use to ensure that the quality of their tests is accurate and precise. This entails proper and effective management of testing processes meant to authenticate results that come out of such tests
Introduction to basic statistics for test laboratories
Errors vs. uncertainties
Accuracies and precision
Distributions important to test/analytical labs
Samples vs. populations
Confidence limits/intervals, level of confidence
Hypothesis testing
Q-test and G-test for outliers
F-tests, T-tests
Correlations and regression in the analytical test laboratory calibration
Standards
Number of Standards
Inclusion of the blank
Range of Standards
Linearity
Correlation coefficient
Significant linearity – ANOVA
Regression Parameters
Slope, intercept, regression line
Calibration uncertainties
Method sensitivity
Limit of detection – LOD
Limit of quantization – LOQ
Specificity vs. selectivity
Determination
Repeatability uncertainties
Regression uncertainty
Minimization of the regression uncertainty
How to handle dilutions
Method of standard additions
Using Regression to compare the results of two methods/analysts/instruments
Analysis of Variance – ANOVA
Single factor, Two factor – ANOVA
Application in providing the competency of analysts, instruments and method
Control charts
Stewart charts
Range charts
Cusum Charts
Evaluation of the uncertainty of measurement
Identification of uncertainties. Quantifying, combining and extending uncertainties
Typical uncertainties: Mass, Volume, Purity uncertainties
Repeatability uncertainties
Method validation
Setting validation criteria
Parameters to be validated
Precision
Working range
Linearity
Limit of detection – LOD
Limit of quantization – LOQ
Sensitivity
Selectivity and specificity
Ruggedness
Robustness
Uncertainties
Stability of stock solution
Traceability