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Review
. 2012;12(7):8966-86.
doi: 10.3390/s120708966. Epub 2012 Jun 29.

Study Designs and Statistical Analyses for Biomarker Research

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Free PMC article
Review

Study Designs and Statistical Analyses for Biomarker Research

Masahiko Gosho et al. Sensors (Basel). .
Free PMC article

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.

Figures

Figure 1.
Figure 1.
Biomarker types. (a) Prognostic biomarker, (b) predictive biomarker, (c) pharmacodynamic biomarker, (d) surrogate endpoint. ‘S’ and ‘T’ denote standard and test treatments, respectively.
Figure 1.
Figure 1.
Biomarker types. (a) Prognostic biomarker, (b) predictive biomarker, (c) pharmacodynamic biomarker, (d) surrogate endpoint. ‘S’ and ‘T’ denote standard and test treatments, respectively.
Figure 2.
Figure 2.
Biomarker by treatment interaction design.
Figure 3.
Figure 3.
Biomarker-strategy design. (a) With standard control and (b) with randomized control. ‘+’ and ‘−’ correspond to the respective positive and negative biomarker statuses.
Figure 4.
Figure 4.
(a) Enrichment study design and (b) hybrid design. ‘+’ and ‘−’ correspond to the respective positive and negative biomarker statuses.
Figure 5.
Figure 5.
General procedure for adaptive signature and biomarker-adaptive threshold designs. ‘S’ and ‘T’ denote the standard and test treatments, respectively.
Figure 6.
Figure 6.
General procedure for adaptive accrual design. ‘S’ and ‘T’ denote the standard and test treatments, respectively.
Figure 7.
Figure 7.
Framework for Bayesian adaptive design. Patients are assigned to biomarker groups 1–4 in sequential order according to the characteristics of the three biomarker categories. ‘+’ and ‘−’ correspond to the respective positive and negative biomarker statuses. Patients are adaptively randomized to one of the three treatments according to their biomarker groups. The dashed arrows indicate the putative effective treatment for each of the biomarker groups.

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References

    1. Ioannidis J.P., Panagiotou O.A. Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. J. Am. Med. Assoc. 2011;305:2200–2210. - PubMed
    1. Biomarkers Definitions Working Group Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 2001;69:89–95. - PubMed
    1. Frank R., Hargreaves R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2003;2:566–580. - PubMed
    1. Hayes D.F., Bast R.C., Desch C.E., Daniel F., Fritsche H., Kemeny N.E., Jessup J.M., Locker G.Y., Macdonald J.S., Mennel R.G., et al. Tumor marker utility grading system: A framework to evaluate clinical utility of tumor markers. J. Natl. Cancer Inst. 1996;88:1456–1466. - PubMed
    1. Jenkins M., Flynn A., Smart T., Harbron C., Sabin T., Ratnayake J., Delmar P., Herath A., Jarvis P., Matcham J. On behalf of the PSI Biomarker Special Interest Group. A statistician's perspective on biomarkers in drug development. Pharm. Stat. 2011;6:494–507. - PubMed

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