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Sensors 2012, 12(7), 8966-8986; doi:10.3390/s120708966
Review

Study Designs and Statistical Analyses for Biomarker Research

1,* , 1,2
 and 3
Received: 15 May 2012; in revised form: 21 June 2012 / Accepted: 21 June 2012 / Published: 29 June 2012
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Abstract: 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.
Keywords: biomarker adaptive design; confounding; multiplicity; predictive factor; statistical test biomarker adaptive design; confounding; multiplicity; predictive factor; statistical test
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Gosho, M.; Nagashima, K.; Sato, Y. Study Designs and Statistical Analyses for Biomarker Research. Sensors 2012, 12, 8966-8986.

AMA Style

Gosho M, Nagashima K, Sato Y. Study Designs and Statistical Analyses for Biomarker Research. Sensors. 2012; 12(7):8966-8986.

Chicago/Turabian Style

Gosho, Masahiko; Nagashima, Kengo; Sato, Yasunori. 2012. "Study Designs and Statistical Analyses for Biomarker Research." Sensors 12, no. 7: 8966-8986.



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