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Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range;
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& Mathematical Sciences at the Nanyang Technological University, Singapore. Her research interests include survival analysis, longitudinal/clustered data;
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, Singapore. Her research interests include survival analysis, longitudinal/clustered data analysis and mixture models. Kelvin Kai-wing Yau;
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Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions;
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Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions;
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This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival;
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techniques required to study such data sets. aeo First book to focus on multilevel modelling for health and medical research aeo Covers the;
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survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses;
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survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses;
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Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical;
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Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical;
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This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h;
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Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research;
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case studies. Following an overview of medical imaging for Rapid Prototyping, the book goes on to discuss working with medical scan data and;
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submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and;
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This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily;
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interactions of scholars across health research related disciplines. Topics covered include: Health data analysis and applications to EHR data;
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coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series;
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coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series;
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standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a;
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, quality control of spatial data, and related research issues in spatial analysis Spatial statistical solutions in spatial data quality;
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, quality control of spatial data, and related research issues in spatial analysis Spatial statistical solutions in spatial data quality;
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-of-the-art in the various multi-disciplinary solutions for Medical Image Processing and Computational Vision, including research, techniques;
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survey data, time series data, and survival data * Perform database management in STATA In Detail STATA is an integrated software package;
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used, including medical statisticians and economists. * Includes numerous examples using real data from medical and health research, and;
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, medical terminology, and laboratory data book all in one, the Guide helps you organize case information, improve problem-solving skills, learn;
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Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly;
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