Automated data analytics workflow for stability experiments based on regression analysis.

INTRODUCTION: We define a designated data analytics workflow for the evaluation of stability experiments, which takes all data situations into account. This complements the evaluation described by the CLSI EP25 [1] guideline by including a targeted exception handling algorithm and thus allows one to automatically evaluate stability data based on linear regression analysis. DESCRIPTION: The evaluation of stability experiments based on regression analysis requires the calculation of the confidence interval of the regression line. The stability time is estimated at the intersection of the confidence interval with the acceptance criterion. This approach results in solving a quadratic equation, with factors that depend on the estimated intercept, slope, the measurement variability and the chosen timepoints. When defining an automated data analytics workflow for this problem, the different cases for the solutions of the quadratic equation must be considered. For some data situations there might be no solution at all, other data situations result in a negative and a positive solution and finally there might be even two positive solutions. All these cases have to be considered for the choice of the right solution to become the estimated stability time. The CLSI EP25 [1] guideline on stability evaluation of in vitro diagnostic reagents addresses this problem only superficially and might even lead to incorrect results for some specific data scenarios. RESULTS: We evaluate all possible data scenarios and provide examples for each. Based on the gained theoretical insights, we define a designated data analytics workflow and visualize it with a flowchart. By following this flowchart one can implement an automated analysis workflow, targeting all data scenarios with the appropriate exception handling. DISCUSSION: We deduce that the description for obtaining stability times according to CLSI EP25 is not fully adequate, as it addresses only best-case scenarios. However, for automated data analytics workflows all possible data situations have to be considered. With the here presented workflow one can program automated data analytics pipelines, which ensure that the right stability time is obtained, in case it exists. In addition all exceptions, where no stability times are present, are addressed in the right way and it provides hints as to the failure reason.