Sampling Errors in Validation.

Recognition of sampling errors and their impact on data variation is an important consideration in pharmaceutical and medical device validation. The evaluation of suspect data may be enhanced by consideration of sampling errors. Validation and compliance professionals must always be aware of the pot...

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Publicado en:Journal of Validation Technology (JVT) Vol. 17; no. 1; pp. 81 - 89
Autor principal: Smith, Patricia L.
Formato: tables/charts Journal Article
Publicado: Institute of Validation Technology Winter2011
Acceso en línea:Ver este registro en EBSCOhost
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      place: Duluth, Minnesota
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        atl: Sampling Errors in Validation.
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        au: Smith, Patricia L.
      sug:
        subj:
          Sampling Error
          Quality Assurance
          Technology, Pharmaceutical
          Analytic Sample Preparation Methods
          Laboratories
          Software
          Data Management
      ab: Recognition of sampling errors and their impact on data variation is an important consideration in pharmaceutical and medical device validation. The evaluation of suspect data may be enhanced by consideration of sampling errors. Validation and compliance professionals must always be aware of the potential for sampling errors and their impact. Sampling errors may greatly influence validation data, cause incorrect judgments, influence trending, add variation, and otherwise significantly affect data accuracy and precision. Sampling errors may be generally categorized as Material Errors, Process Errors, Sample Errors, and Laboratory Errors. Ten specific individual sampling errors have been identified. Material errors include Fundamental Error, Grouping and Segregation Error, and Nugget Effect. Material variation is due to its composition, its distribution, and inherent material non-uniformity. Material variation is often not fully appreciated and may impact all other sources of variation. Process errors include Non-periodic Process Errors and Periodic Process Errors. Process variation is fairly well understood and contributes to data variation in generally predictable ways. Sample errors include Sample Definition, Sample Collection, and Sample Handling. Sample selection (i.e., sample definition and collection) is often not defined to a sufficient extent to prevent errors and excessive data variation. Laboratory errors include Analytical Error and Data Error. Laboratory error contributions to sampling errors are frequently overlooked. Data errors may occur in processing, sampling, and in the laboratory. Example actual occurrences of sampling errors directly impacting validation are described.
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        tables/charts
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    language: English
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