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Big data. --- Medical statistics.

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Biomathematics. Biometry. Biostatistics --- Human medicine --- Medical statistics.

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1.Basic concepts - 2.Test of significance - 3.Fisher's test for 2 X 2 contingency tables - 4.Approximate significance tests for contigency tables - 5.Some warnings concerning 2 X 2 tables - 6.Kaplan-Meier or 'actuarial' survival curves - 7.The log-rank or Mantel-Haenszel test for the comparison of survival curves - 8. An introduction to the normal distribution - 9.Analyzing normally distributed data - 10.Linear regression models for medical data - 11. Binary logistic regression - 12.Regression models for count data - 13.Proportional hazards regression - 14. The analysis of longitudinal data - 15.Analysis of variance - 16.Data analysis - 17.The question of sample size - 18.The design of clinical trials - 19.Further comments regarding clinical trials - 20.Meta-analysis - 21.Epidemiological applications - 22.Diagnostic tests - 23.Agreement and reliability - 24.References - 25. Subject index

Medical statistics --- Biometry --- Statistics as Topic --- methods --- Biometry - methods

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Medical statistics. --- Health --- Health statistics --- Medicine --- Statistics --- Statistical methods

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This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

Medical statistics --- Biometry --- Data Interpretation, Statistical --- Models, Statistical --- Data processing --- Medical statistics. --- Biometry. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Health --- Health statistics --- Medicine --- Statistical methods --- Data processing. --- Medical statistics - Data processing --- Biometry - Data processing