Advances in Statistical Methods for the Health Sciences: by Douglas G. Altman, Marialena Trivella, Francesco Pezzella, PDF

By Douglas G. Altman, Marialena Trivella, Francesco Pezzella, Adrian L. Harris (auth.), Jean-Louis Auget, N. Balakrishnan, Mounir Mesbah, Geert Molenberghs (eds.)

ISBN-10: 0817643680

ISBN-13: 9780817643683

ISBN-10: 081764542X

ISBN-13: 9780817645427

Statistical tools became more and more very important and now shape indispensable a part of study within the overall healthiness sciences. Many subtle methodologies were built for particular purposes and difficulties. This self-contained quantity, an outgrowth of an "International convention on Statistical tools in wellbeing and fitness Sciences," covers a variety of issues concerning new statistical equipment and novel purposes within the future health sciences.

The chapters, written through top specialists of their respective fields, are thematically divided into the subsequent areas:

* Prognostic reports and common epidemiology

* Pharmacovigilance

* caliber of life

* Survival analysis

* Clustering

* security and efficacy assessment

* medical design

* types for the environment

* Genomic analysis

* Animal health

This finished quantity might be hugely priceless an of significant curiosity to the overall healthiness technology group in addition to practitioners, researchers, and graduate scholars in utilized likelihood, statistics, and biostatistics.

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Additional info for Advances in Statistical Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis

Example text

Systematic reviews of evaluations of prognostic variables, British Medical Journal, 323, 224–228. 2. Altman, D. , and Schumacher, M. (1994). Dangers of using “optimal” cutpoints in the evaluation of prognostic factors, Journal of the National Cancer Institute, 86, 829–835. 3. Altman, D. , and Lyman, G. H. (1998). Methodological challenges in the evaluation of prognostic factors in breast cancer, Breast Cancer Research Treatment, 52, 289–303. 4. , and Friedenreich, C. (1999). Traditional reviews, meta-analyses and pooled analyses in epidemiology, International Journal of Epidemiology, 28, 1–9.

Angiogenesis as a prognostic factor in non-small cell lung carcinoma. A meta-analysis using individual patient data, Submitted for publication. 30. Williamson, P. , Smith, C. , Hutton, J. , and Marson, A. G. (2002). Aggregate data meta-analysis with time-to-event outcomes, Statistics in Medicine, 21, 3337–3351. 2 On Statistical Approaches for the Multivariable Analysis of Prognostic Marker Studies N. Holl¨ ander and W. Sauerbrei Institute of Medical Biometry and Medical Informatics, Freiburg, Germany Abstract: Various statistical methods to analyse prognostic marker studies are available and are used in practice.

Bendahl, P. , Meijer-van Gelder, M. , Fiets, W. , Blankenstein, M. , Beex, L. , Klijn, J. , and Foekens, J. A. (2002). Pooled analysis of prognostic impact of urokinasetype plasminogen activator and its inhibitor PAI-1 in 8377 breast cancer patients, Journal of the National Cancer Institute, 94, 116–128. 13. McKibbon, K. , and Wilczynski, N. (1995). Beyond ACP Journal Club: How to harness MEDLINE for prognosis problems, ACP Journal Club, 123. A12–A14. 14. McShane, L. , Altman, D. , Taube, S. , Clark, G.

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Advances in Statistical Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis by Douglas G. Altman, Marialena Trivella, Francesco Pezzella, Adrian L. Harris (auth.), Jean-Louis Auget, N. Balakrishnan, Mounir Mesbah, Geert Molenberghs (eds.)


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