Study Designs and Statistical Analyses for Biomarker Research

被引：52

作者：

Gosho, Masahiko ^{[1
]}

Nagashima, Kengo ^{[1
,2
]}

Sato, Yasunori ^{[3
]}

机构：

[1] Tokyo Univ Sci, Grad Sch Engn, Shinjuku Ku, Tokyo 1628601, Japan

[2] Josai Univ, Fac Pharmaceut Sci, Sakado, Saitama 3500295, Japan

[3] Chiba Univ Med, Clin Res Ctr, Chuo Ku, Chiba 2608677, Japan

来源：

SENSORS | 2012年 / 12卷 / 07期

基金：

日本学术振兴会;

关键词：

biomarker adaptive design; confounding; multiplicity; predictive factor; statistical test; CLINICAL-TRIAL DESIGNS; FALSE DISCOVERY RATE; SURROGATE ENDPOINTS; GENE-EXPRESSION; BREAST-CANCER; LUNG-CANCER; VALIDATION; POLYMORPHISMS; CHEMOTHERAPY; PROGNOSIS;

D O I：

10.3390/s120708966

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页码：8966 / 8986

页数：21

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