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OOS Investigation | Detecting and Treating Outliers

The investigation of out-of-specification (OOS) results is an important part of the work undertaken by the analytical laboratory. 

The OOS process is concerned with the examination of any result that falls outside established acceptance criteria. This either relates to acceptance criteria (specifications) established in official compendia, such as pharmacopeia, or by an organization. 

The scope of the OOS also includes acceptance criteria established in drug applications and drug master files. 

There are other associated terms, such as out of trend (OOT) and outlier, to evaluate .

An outlier is defined as an observation that "appears" to be inconsistent with other observations in the data set. 

An outlier has a low probability that it originates from the same statistical distribution as the other observations in the data set. 

On the other hand, an extreme value is an observation that might have a low probability of occurrence but cannot be statistically shown to originate from a different distribution than the rest of the data.

Outliers can provide useful information about the process. 

An outlier can be created by a shift in the location (mean) or in the scale (variability) of the process. 

Though an observation in a particular sample might be a candidate as an outlier, the process might have shifted. 

Sometimes, the spurious result is a gross recording error or a measurement error. 

Measurement systems should be shown to be capable for the process they measure. 

Outliers also come from incorrect specifications that are based on the wrong distributional assumptions at the time the specifications are generated.

Statistical outlier detection has become a popular topic as a result of the US Food and Drug Administration's out of specification (OOS) guidance and increasing emphasis on the OOS procedures of pharmaceutical companies. 

When a test fails to meet its specifications, the initial response is to conduct a laboratory investigation to seek an assignable cause. 

As part of that investigation, an analyst looks for an observation in the data that could be classified as an outlier. 

The FDA guidance "Investigating Out of Specification (OOS) Test Results for Pharmaceutical Production" and the US Pharmacopeia are clear that a chemical result cannot be omitted with an outlier test, but that a bioassay can be omitted with an outlier test. 

The two areas specifically prohibited from outlier tests are content uniformity and dissolution testing.

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