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Related Concept Videos

Reliability and Validity01:29

Reliability and Validity

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Nursing Clinical Information System (NCIS)
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Clinical biomarker validation.

John L Allinson1

  • 1Biomarker Specialist and Consultant, Flatfield Lodge, Hull Road, Howden, DN14 7LP, East Riding of Yorkshire, UK.

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Summary
This summary is machine-generated.

Bioanalytical laboratories face challenges with biomarker assays due to unclear validation guidelines and limited clinical expertise. Proper validation is crucial for reliable biomarker data in drug development.

Keywords:
biomarkercontext-of-usephysiologyquality controlregulatory guidancestatisticsvalidation

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Area of Science:

  • Bioanalytical Chemistry
  • Clinical Diagnostics
  • Drug Development

Background:

  • Bioanalytical laboratories traditionally focus on pharmacokinetic (PK) analysis for drug development.
  • The integration of biomarker assays presents new challenges, differing from traditional PK services.
  • A lack of specific regulatory guidance for biomarker assay validation and limited clinical expertise within bioanalytical labs are key issues.

Purpose of the Study:

  • To review different analytical laboratory types and their practices concerning biomarker assays.
  • To highlight the importance of correct biomarker assay validation in drug development.
  • To demonstrate potential negative outcomes of improperly validated biomarker assays through case studies.

Main Methods:

  • Review of existing literature and practices in clinical and bioanalytical laboratories.
  • Analysis of regulatory landscapes concerning biomarker assay validation.
  • Presentation of case studies illustrating the impact of validation quality.

Main Results:

  • Significant challenges exist for bioanalytical labs adopting biomarker assays.
  • Inadequate validation of biomarker assays can lead to unreliable data.
  • Historical practices in clinical labs offer insights but require adaptation for drug development.

Conclusions:

  • Clear regulatory guidelines are needed for biomarker assay validation.
  • Enhanced training and collaboration are essential for bioanalytical scientists in the clinical arena.
  • Correctly validated biomarker assays are critical for successful drug development outcomes.