Statistics in biomedical research

Carmen Cadarso-Suárez; Wenceslao González-Manteiga

doi:10.3989/arbor.2007.i725.108

Authors

Carmen Cadarso-Suárez Biostatistics Unit, Department of Statistics and Operations Research, University of Santiago de Compostela
Wenceslao González-Manteiga Biostatistics Unit, Department of Statistics and Operations Research, University of Santiago de Compostela

DOI:

https://doi.org/10.3989/arbor.2007.i725.108

Keywords:

Statistics, Epidemiology, Clinical Trials, Bionformatics, training programs

Abstract

The discipline of biostatistics is nowadays a fundamental scientific component of biomedical, public health and health services research. Traditional and emerging areas of application include clinical trials research, observational studies, physiology, imaging, and genomics. The present article reviews the current situation of biostatistics, considering the statistical methods traditionally used in biomedical research, as well as the ongoing development of new methods in response to the new problems arising in medicine. Clearly, the successful application of statistics in biomedical research requires appropriate training of biostatisticians. This training should aim to give due consideration to emerging new areas of statistics, while at the same time retaining full coverage of the fundamentals of statistical theory and methodology. In addition, it is important that students of biostatistics receive formal training in relevant biomedical disciplines, such as epidemiology, clinical trials, molecular biology, genetics, and neuroscience.

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References

Abraira, V., Cadarso, C., Gómez, G., Martín, A. and Pita, S. (2001): “Mesa Redonda: La Estadística en la Investigación Médica”, Qu.estii., 25, 121-156.

Altman, D. G. and Goodman, S. (1994): “Transfer of technology from statistical journals to the biomedical literature: past trends and future predictions”, Journal of the American Medical Association, 272-129.

Andersen, P. K., Borgan, O., Gill, R. D. and Keiding, N. (1993): Statistical Models Based on Counting Processes, Springer, New York.

Anello, Ch. (1999): “Emerging and recurrent issues in drug development”, Statistics in Medicine, 18, 2301-2309. doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18<2301::AID-SIM256>3.0.CO;2-X

Cheng. B. and Titterington, D. M. (1994): “Neural networks: a review from a statistical perspective”, Statistical Science, 9, 2-54. doi:10.1214/ss/1177010638

Cressie, N. (1993): Statistics for Spatial Data, Revised Edition, Wiley, New York.

DeMets, D. L., Stormo, G., Boehnke, M., Louis, T. A., Taylor, J. and Dixon, D. (2006): “Training of the next generation of biostatisticians”, Statistics in Medicine, 25, 3415-3429. doi:10.1002/sim.2668

Ewens, W. J. and Grant, G. (2005): Statistical Methods in Bioinformatics (Statistics for Biology and Health), Springer, New York.

Ferraty, F. and Vieu, Ph. (2006): Nonparametric functional data analysis: Theory and practice, Springer Series in Statistics, Springer, New York.

Greenhouse, S. W. (2003): “The growth and future of biostatistics: (A view from the 1980s)”, Statistics in Medicine, 22, 3323-3335. doi:10.1002/sim.1634

Hastie, T. J. and Tibshirani, R. J. (1990): Generalized Additive Models, London, Chapman and Hall.

Hougaard, P. (2000): Analysis of Multivariate Survival Data, Springer, New York.

Houwelingen (van), H. C. (1997): “The future of Biostatistics: expecting the unexpected”, Statistics in Medicine, 16, 2773-2784.

Klein, J. P. and Moeschberger, M. L. (2003): Survival Analysis: Techniques for Censored and Truncated Data, Second Edition, Wiley, New York.

Lawson, A. (2006): Statistical Methods in Spatial Epidemiology, 2nd edition, Wiley.

Lawson, A. and Cressie, N. (2000): “Spatial statistical methods for environmental epidemiology”, in Handbook of Statistics, Vol. 18, P.K. Sen & C.R. Rao (Eds.), Elsevier, Amsterdam, pp. 357-396.

Ohno-Machado, L. (1996): Medical Applications of Artificial Neural Networks: Connectionist Model of Survival, Ph.D Dissertation, Stanford University.

Ramsay, J. and Silverman, B. (2002): Applied functional data analysis; methods and case studies, Springer, New York. doi:10.1007/b98886

Ramsay, J. and Silverman, B. (2005): Functional data analysis, 2nd edition, Springer, New York.

Ripley, B. D. (1993): “Statistical aspects of neural networks”, in Barndorff-Nielsen, O. E., Jensen, J. L., Kendall, W. S. (eds.), Networks and Chaos-Statistical and Probabilistic Aspects, London, Chapman & Hall, 40-123.

Ripley, B. D. (1996): Pattern Recognition and Neural Networks, Cambridge University Press.

Vapnik, V. (1995): The Nature of Statistical Learning Theory, Springer-Verlag.

Zelen, M. (2006): “Biostatisticians, Biostatistical Science and the Future”, Statistics in Medicine, 25: 3409-3414. doi:10.1002/sim.2658