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What is the Difference Between OOS, OOE and OOT?

The terms OOS, OOE, and OOT always emerge in connection with the handlingof deviating analysis results. But what are their definitions?

Out-Of-Specifications (OOS):
  • This is a result that does not meet the established acceptance criteria outlined in official compendia or company documentation. A result falls outside established acceptance criteria established in official compendia and/or by company documentation.

Out-Of-Expectation (OOE):
  • When a series of results over a short period shows an atypical, aberrant, or anomalous pattern, this is called an OOE result. Typically, an OOE result is one in which the specifications are met, but the analysis is not within the range of variability expected.
  • An atypical, aberrant, or anomalous result within a series of results obtained over a short period of time is an OOE result. An OOE result is a result that meets specifications but is outside the expected variability of the analytical procedure.


Out-Of-Trend (OOT):
  • The result does not fall within a prediction interval or fails to meet the statistical processcontrol criteria. A time dependent result which falls outside a prediction interval or fails a statistical processcontrol criterion.
  • A trend is a sequence of temporal procedures, e.g. for manufacturing different batches of a product. There are two types of trends:
  1. In one case, no trend is expected, e.g. in production or when analysing process data whereeveryone expects that they are under statistical control.
  2. In the other case, a trend is expected. One typical example for that is stability testing whereone expects that the content of the API reduces over the storage period, or that the quantityof impurities increases over time.
There is a fundamental difference between these two types of OOT results: indeed, in the second situation, the dispersion increases over time.

  • A more recent concern is being out-of-trend (OOT). An OOT situation exists when one or more reportable results, collected over a period of time, are compared with a historical dataset or with a statistical model and determined to be different from a practical significance endpoint.
  • The cause behind OOT occurrences can take several forms. The process could experience a sudden change in the average or have an outlying data point. The standard deviation of the data could increase or decrease. The general direction of the data could be gradually upward or downward, which is often called process drift. 
  • Finally, the process could cycle up and down. Cycling can occur if the operators over control for the critical process parameters (also known as independent variables or factors) and actually induce additional variability into the critical quality attributes (also known as dependent variables or responses). 
  • Note that OOT is usually perceived as a negative event but it could be a positive one. If the variability decreases or if the average moves towards the target, that would be a beneficial trend. If stability data does not change over the course of the study, that would be a neutral trend. These changes need not reach the level of statistical significance to raise an alert and initiate a response.

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