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Study Designs and Statistical Analyses for Biomarker Research

Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important...

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Detalles Bibliográficos
Autores principales: Gosho, Masahiko, Nagashima, Kengo, Sato, Yasunori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444086/
https://www.ncbi.nlm.nih.gov/pubmed/23012528
http://dx.doi.org/10.3390/s120708966
Descripción
Sumario:Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important aspects related to study design and statistical analysis for clinical research incorporating biomarkers. We describe the typical and current study designs for exploring, detecting, and utilizing biomarkers. Furthermore, we introduce statistical issues such as confounding and multiplicity for statistical tests in biomarker research.