Abstract
Biomarkers are playing an increasingly important role throughout many aspects of the pharmaceutical discovery and development pipeline. They have many differing roles and applications and statistics plays a critical role in their discovery, validation or qualification and how they are applied and utilised. In this chapter we shall discuss what biomarkers are, the types of data that they generate and the impact on the subsequent statistical analysis, paying particular attention to the avoidance of false positives in biomarker discovery and confirming the technical performance of assays measuring biomarkers.
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References
Atkinson AJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, Oates JA, Peck CC, Schooley RT, Spilker BA, Woodcock J, Zeger SL (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89–95
Baggerly K, Coombes K (2008) Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. J Clin Oncol 26:1186–1187
Breiman L (2001) Random forests. Mach Learn 45:5–32
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Frank R, Hargreaves R (2003) Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2:566–580
Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378
Hand DJ (2009) Measuring classier performance: a coherent alternative to the area under the ROC curve. Mach Learn 123:77–103
Jenkins M, Flynn A, Smart T, Harbron C, Sabin T, Ratnayake J, Delmar P, Herath A, Jarvis P, Matcham J (2011) A statistician’s perspective on biomarkers in drug development. Pharm Stat 6:494–507
Molinaro AM, Lostritto K, van der Laan M (2010) partDSA: deletion/substitution/addition algorithm for partitioning the covariate space in prediction. Bioinformatics 26:1357–1363
Prentice R (1989) Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med 8:431–440
Shi L et al (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28:827–838
Storey J (2002) A direct approach to false discovery rates. J R Stat Soc Ser B 64(3):479–498
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67:301–320
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Harbron, C. (2016). Biomarkers. In: Zhang, L. (eds) Nonclinical Statistics for Pharmaceutical and Biotechnology Industries. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-319-23558-5_14
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DOI: https://doi.org/10.1007/978-3-319-23558-5_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23557-8
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