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Statistical methods --- Simulation models --- Models --- Probability analysis --- Epidemiology --- Disease surveillance --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Models, Statistical --- Statistics as Topic --- Disease --- epidemiology --- Mathematical statistics --- Models, Statistical. --- Epidemiology. --- Statistical methods. --- Social Epidemiology --- Epidemiologies, Social --- Epidemiology, Social --- Social Epidemiologies --- Models, statistical

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The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies .

Medical statistics. --- Medicine --- Evidence-based medicine --- Research --- Statistical methods. --- Statistics. --- Internal medicine. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Internal Medicine. --- Medicine, Internal --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics . --- Models, Statistical. --- Regression Analysis. --- Analysis, Regression --- Regression Diagnostics --- Statistical Regression --- Analyses, Regression --- Diagnostics, Regression --- Regression Analyses --- Regression, Statistical --- Regressions, Statistical --- Statistical Regressions --- Statistics as Topic --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Health Workforce --- Statistics --- Health --- Health statistics

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In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to m

Brain mapping --- Brain --- Statistical methods --- Imaging --- Statistical methods. --- Cerebrum --- Mind --- Central nervous system --- Head --- Connectome mapping --- Mapping of the brain --- Topographic brain mapping --- Imaging&delete& --- Localization of functions --- Brain mapping. --- Statistics --- Mathematical models. --- Graphic methods. --- Diagrams, Statistical --- Statistical diagrams --- Curve fitting --- Brain Mapping --- Image Processing, Computer-Assisted --- Magnetic Resonance Imaging --- Models, Neurological --- Models, Statistical --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Statistics as Topic --- Model, Neurological --- Neurologic Model --- Neurological Model --- Neurological Models --- Neurologic Models --- Model, Neurologic --- Models, Neurologic --- methods --- Brain mapping - Statistical methods --- Brain - Imaging - Statistical methods

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This is a practical introduction to multilevel analysis suitable for all those doing research. Most books on multilevel analysis are written by statisticians, and they focus on the mathematical background. These books are difficult for non-mathematical researchers. In contrast, this volume provides an accessible account on the application of multilevel analysis in research. It addresses the practical issues that confront those undertaking research and wanting to find the correct answers to research questions. This book is written for non-mathematical researchers and it explains when and how to use multilevel analysis. Many worked examples, with computer output, are given to illustrate and explain this subject. Datasets of the examples are available on the internet, so the reader can reanalyse the data. This approach will help to bridge the conceptual and communication gap that exists between those undertaking research and statisticians.

Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Analysis of variance --- Medical statistics --- Medicine --- Biomedical Research --- Models, Statistical --- Multivariate Analysis --- Statistics as Topic --- multivariaat --- regressie-analyse --- wiskundige statistiek --- Analyses, Multivariate --- Analysis, Multivariate --- Multivariate Analyses --- Biomedical research --- Medical research --- Health --- Health statistics --- Statistics --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design --- Research --- methods --- Statistical methods --- Models, Statistical. --- Multivariate Analysis. --- Analysis of variance. --- Medical statistics. --- methods. --- Research. --- Health Workforce --- Canonical Correlation --- Canonical Correlations --- Correlation, Canonical --- Health Sciences --- General and Others

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Mathematical statistics --- Statistique mathématique --- Statistiek. --- Statistik. --- Zeitschrift. --- Taiwan. --- Statistische Methode. --- Statistische Methodenlehre. --- Theorie. --- Statistics as Topic --- Models, Statistical --- Model, Statistical --- Models, Binomial --- Models, Polynomial --- Statistical Model --- Probabilistic Models --- Statistical Models --- Two-Parameter Models --- Binomial Model --- Binomial Models --- Model, Binomial --- Model, Polynomial --- Model, Probabilistic --- Model, Two-Parameter --- Models, Probabilistic --- Models, Two-Parameter --- Polynomial Model --- Polynomial Models --- Probabilistic Model --- Two Parameter Models --- Two-Parameter Model --- Area Analysis --- Correlation Studies --- Correlation Study --- Correlation of Data --- Data Analysis --- Estimation Technics --- Estimation Techniques --- Indirect Estimation Technics --- Indirect Estimation Techniques --- Multiple Classification Analysis --- Service Statistics --- Statistical Study --- Statistics, Service --- Tables and Charts as Topic --- Analyses, Area --- Analyses, Data --- Analyses, Multiple Classification --- Analysis, Area --- Analysis, Data --- Analysis, Multiple Classification --- Area Analyses --- Classification Analyses, Multiple --- Classification Analysis, Multiple --- Data Analyses --- Data Correlation --- Data Correlations --- Estimation Technic --- Estimation Technic, Indirect --- Estimation Technics, Indirect --- Estimation Technique --- Estimation Technique, Indirect --- Estimation Techniques, Indirect --- Indirect Estimation Technic --- Indirect Estimation Technique --- Multiple Classification Analyses --- Statistical Studies --- Studies, Correlation --- Studies, Statistical --- Study, Correlation --- Study, Statistical --- Technic, Estimation --- Technic, Indirect Estimation --- Technics, Estimation --- Technics, Indirect Estimation --- Technique, Estimation --- Technique, Indirect Estimation --- Techniques, Estimation --- Techniques, Indirect Estimation --- Statistics as Topic. --- Models, Statistical. --- Mathematical statistics. --- Statistik --- Zeitschrift --- Periodikum --- Zeitschriften --- Presse --- Fortlaufendes Sammelwerk --- Mathematische Statistik --- Statistische Mathematik --- Statistische Methode --- Statistisches Verfahren --- Statistiken --- Datensammlung --- Stochastik --- Wahrscheinlichkeitsrechnung --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistique mathématique