Out-of-Trend Identification and Removal using Regression Analysis and Closed Loop Approach Methods.

Out-of-Trend Identification and Removal using Regression Analysis and Closed Loop Approach Methods.

Historical Approaches to OOT

The following are typical historical approaches to OOT identification and removal, though they are not recommended approaches. They are not considered to be statistically sound procedures for OOT identification and removal. The difference between consecutive results is outside of half the difference between the prior result and the specification

The result is outside ±5% of initial result

The result is outside ±3% of previous result

The result is outside ±5% of the mean of all previous results.

Closed-Loop Approach to OOT identification and removal

A best-practice approach to OOT determination and removal is to see it as a part of a closed-loop control system during stability monitoring and expiry prediction. The five steps to a closed loop system for OOT are:

·        Addition of new time points and data

·        OOT identification

·        OOT determination and point removal where warranted

·        OOT verification and evaluation of OOT influence

·        Stability and performance prediction.

Addition of New Time Points and Data, Closed Loop

As each new time point is added to the stability analysis, the time point should be checked for OOT potential. If they are within the criteria for OOT identification, then rates of change, expiry, etc. are determined. OOT identification, determination, and verification are used if new time points appear to be suspect.

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