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Advances in Statistical Methods for the Health Sciences: by Jean-Louis Auget, N. Balakrishnan, Mounir Mesbah, Geert

By Jean-Louis Auget, N. Balakrishnan, Mounir Mesbah, Geert Molenberghs

Statistical equipment became an more and more very important and critical a part of study within the healthiness sciences. Many subtle methodologies were built for particular functions and difficulties. This self-contained complete quantity covers a variety of issues relating new statistical tools within the healthiness sciences, together with epidemiology, pharmacovigilance, caliber of existence, survival research, and genomics.

The publication will serve the healthiness technology neighborhood in addition to practitioners, researchers, and graduate scholars in utilized chance, information, and biostatistics.

 

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Extra resources for Advances in Statistical Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis (Statistics for Industry and Technology)

Example text

Yamazaki, Hokkaido University School of Medicine, Kitaku, Sapporo, Japan. 16 D. G. Altman et al. References 1. Altman, D. G. (2001). 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.

Consequently, prognostic markers derived from these studies are often not accepted for general use [see Wyatt and Altman (1995) and Boracchi and Biganzoli (2003)]. In this chapter, we show for a specific example that the choice of the statistical method and various specific assumptions have a strong influence on the results and, therefore, on the interpretation of prognostic markers. Focusing on the statistical analysis of prognostic markers in oncology, we discuss issues of model building in the framework of regression models and of classification and regression trees.

1 Introduction The identification and assessment of prognostic markers (also termed as prognostic factors) is an important task in clinical research. Studies on prognostic markers attempt to determine a prediction of the course of disease for groups of patients defined by the values of prognostic markers. In contrast to therapeutic studies, however, where statistical principles and methods are well developed and generally accepted, this is not the case for the evaluation of prognostic markers. Deficiencies in design [see Riley et al.

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