By Douglas G. Altman, Marialena Trivella, Francesco Pezzella, Adrian L. Harris (auth.), Jean-Louis Auget, N. Balakrishnan, Mounir Mesbah, Geert Molenberghs (eds.)
Statistical tools became more and more very important and now shape indispensable a part of study within the well-being sciences. Many refined methodologies were built for particular functions and difficulties. This self-contained quantity, an outgrowth of an "International convention on Statistical equipment in future health Sciences," covers quite a lot of subject matters touching on new statistical equipment and novel functions within the healthiness sciences.
The chapters, written through top specialists of their respective fields, are thematically divided into the subsequent areas:
* Prognostic stories and basic epidemiology
* caliber of life
* Survival analysis
* security and efficacy assessment
* medical design
* versions for the environment
* Genomic analysis
* Animal health
This entire quantity should be hugely worthwhile an of serious curiosity to the healthiness technology neighborhood in addition to practitioners, researchers, and graduate scholars in utilized likelihood, records, and biostatistics.
Read Online or Download Advances in Statistical Methods for the Health Sciences: Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis PDF
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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
We discuss problems caused by the uncritical application of CART and outline advantages of small and simple trees. Furthermore, we show how to form risk groups with diﬀerent prognoses and we illustrate the necessity to validate results in an independent study. Data of two breast cancer studies are used for illustration. 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.
2004). The speciﬁc features of prognostic investigations are such that, however desirable it might be, applying these general principles is not straightforward. Particular concerns include the inability to extract adequate information about the details of how the study was done, inconsistent methods of data analysis (especially relating to cutpoints and adjustment for other variables), inadequate reporting of the results of the study (linked to the speciﬁc issues for summarising survival data), and the likelihood of publication bias.
Several authors have noted that reviews of published studies are of limited value and that instead reviewers should attempt to acquire the individual patient data from each study; see Altman and Lyman (1998), Altman (2001), Riley et al. (2006), Piedbois and Buyse (2004), and Blettner et al. (1999). The vast majority of systematic reviews are based on published studies, primarily because of the relative ease with which they can be done (even though it is not that easy) [Piedbois and Buyse (2004)].