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Study Designs and Statistical Analyses for Biomarker Research

1
Graduate School of Engineering, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan
2
Faculty of Pharmaceutical Sciences, Josai University, 1-1 Keyakidai, Sakado-shi, Saitama 350-0295, Japan
3
Clinical Research Center, Chiba University of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan
*
Author to whom correspondence should be addressed.
Sensors 2012, 12(7), 8966-8986; https://doi.org/10.3390/s120708966
Received: 15 May 2012 / Revised: 21 June 2012 / Accepted: 21 June 2012 / Published: 29 June 2012
(This article belongs to the Special Issue Biomarkers and Nanosensors: New Approaches for Biology and Medicine)
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. View Full-Text
Keywords: biomarker adaptive design; confounding; multiplicity; predictive factor; statistical test biomarker adaptive design; confounding; multiplicity; predictive factor; statistical test
MDPI and ACS Style

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

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