Titlebook: Analyzing Medical Data Using S-PLUS; Brian Everitt,Sophia Rabe-Hesketh Book 2001 Springer Science+Business Media New York 2001 Analysis of

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https://doi.org/10.1057/97802301187374). These parametric regression methods are widely used, but they may not give faithful data descriptions when the assumptions on which they are based are not met, or in the presence of higher order interactions among some of the explanatory variables.

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Prologue: Medical Statistics, medicine is the collection and valid interpretation of evidence, particularly quantitative evidence in the form of numerical observations and measurements. The proper use of statistical methods is central in this process.

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Generalised linear models II: Poisson regression,, a number of people are followed up and the onset of a disease recorded for each person. The data can then be summarized by counting the total number of occurrences of the disease in different subgroups of subjects sharing the same values of the important explanatory variables. The dependent variable is therefore a count.

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Linear Mixed Models II,stant variances of the random error terms. Finally, we consider models for . data in which there are several levels of clustering. Examples are repeated measures on subjects in hospitals, patients in wards in hospitals, or patients of GPs in cities.

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Regression Trees,4). These parametric regression methods are widely used, but they may not give faithful data descriptions when the assumptions on which they are based are not met, or in the presence of higher order interactions among some of the explanatory variables.

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1431-8776 far less about the details of S-PLUS and probably less technical descriptions of techniques. The book concentrates solely on medical data sets trying to demonstrate the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.978-1-4419-3176-4978-1-4757-3285-6Series ISSN 1431-8776 Series E-ISSN 2197-5671

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,Reading the Trickster’s Footsteps,r 4); their use here allows the data to suggest the form of the relationship involved, and indirectly to suggest whether the data might be better modeled by a linear or generalized linear model which included polynomial terms of a particular degree for some, or all, of the explanatory variables.