Validation data reviews

Enforcement methods provided by the manufacturer are not generally tested in the laboratories of the European regulatory authorities. Very often, proposed methods are evaluated by assessing the logic of proposed procedures and only for the completeness of validation data. For this theoretical review process, as much information as possible should be available. Recovery data from many validation experiments with different kinds of matrices and the resulting chromatograms of control and fortified samples provide the confidence needed by the referee. In the following sections, the most important aspects of this evaluation will be considered. 

This report is the first volume in the series Acute Exposure Guideline Levels for Selected Airborne Chemicals. AEGL documents for four chemicals—aniline, arsine, monomethylhydrazine, and dimethyl hydrazine—are published as an appendix to this report. The subcommittee concludes that the AEGLs developed in those documents are scientifically valid conclusions based on the data reviewed by NAC and are consistent with the NRC guideline reports. AEGL reports for additional chemicals will be presented in subsequent volumes. 

D1 Data review, verification, and validation D2 Validation and verification methods D3 Reconciliation with user requirements... 

Group D elements describe the procedures that will be used for the assessment of data quality and usability. Properly conducted laboratory data review, verification, and data validation establishes whether the obtained data are of the right type, quality, and quantity to support their intended use. 

D1 Data review, verification, and validation D2 Validation and verification methods 4.3.5 Data reduction, verification, and reporting 4.3.6 Internal data review 5.1 Data evaluation 5.2 The seven steps of data evaluation... 

Once the data packages have been delivered to the client, the correction of these errors takes a substantial amount of time and effort that may delay the completion of data validation or review for days. Missing data or insufficient documentation are the worst errors that could be made in the data package preparation. They preclude the reconstruction of laboratory data production process and affect the calculation of data completeness. 

Data review is also a process of reviewing a body of data against a pre-established set of acceptance criteria it is less rigorous than data validation and has fewer acceptance criteria. The acceptance criteria used as standards in data review are also defined in the SAP. 

Data review generally does not have a well-defined scope. The level of diligence during data review may range from Level 3 validation to cursory review of basic... 

Appendices 18, 19, 20, and 21 itemize the type of laboratory data and support documentation that are typically included into the data packages for validation and review. The planning team must specify the data package content requirements in the SAP and in the Laboratory SOW and make sure that they are understood by the laboratory prior to start of field work. 

In the course of data validation, data qualifiers are attached to the data. Data qualifiers are the alphabetic symbols that indentify an undetected compound or a deviation from acceptance criteria. Data qualifiers are also called data flags. The findings of data validation are detailed in a data validation report, which documents the validation process and explains the reasons for attaching the qualifiers to the data. Laboratories also use data qualifiers for indicating deviations from laboratory acceptance criteria. These qualifiers are replaced with the validation qualifiers in the course of data validation. Qualifiers are rarely used in data review. 

Validation and review processes, different as they may be in the level of diligence, follow the same basic steps of consecutive examination of a data package to establish the following facts ... 

This chapter describes the process of data evaluation that is equivalent to data validation Level 3. It may be scaled up to validation Level 4 by including a thorough examination of raw data and the recalculation of results or scaled down to cursory data review. 

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