Mounir Mesbah – författare

Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

During the past three decades, the use of statistical methods in the health sciences has become quite common, and many sophisticated methods have been developed for specific applications and problems. Presented in this work are chapters covering a broad range of recent advances of statistical methods in the health sciences written by internationally renowned experts. The volume is thematically classified into different areas of the health sciences according to the particular focus of the chapters. Specific topics covered include stochastic models in cancer and AIDS studies, statistical methods in genome sequence analysis, bioequivalence studies, randomized clinical trial designs, survival analysis and applications, and statistical methods in quality-of-life. Some chapters are reviews of active research, while others highlight recent developments, new methods, and practical real-world data. This comprehensive reference text will serve the health sciences community, as well as practitioners, researchers, and graduate students in applied probability and biostatistics.

The Volume presents a broad spectrum of papers which illustrates a range of current research related to the theory, methods and applications of health related quality of life (HRQoL) as well as the interdisciplinary nature of this work.

The volume presents a broad spectrum of papers which illustrates a range of current research related to the theory, methods and applications of health related quality of life (HRQoL) as well as the interdisciplinary nature of this work.

Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

Biostatistics and Computer-Based Analysis of Health Data Using the R Software addresses the concept that many of the actions performed by statistical software comes back to the handling, manipulation, or even transformation of digital data.

It is therefore of primary importance to understand how statistical data is displayed and how it can be exploited by software such as R. In this book, the authors explore basic and variable commands, sample comparisons, analysis of variance, epidemiological studies, and censored data.

With proposed applications and examples of commands following each chapter, this book allows readers to apply advanced statistical concepts to their own data and software.

Features useful commands for describing a data table composed made up of quantitative and qualitative variables Includes measures of association encountered in epidemiological studies, odds ratio, relative risk, and prevalence Presents an analysis of censored data, the key main tests associated with the construction of a survival curve (log-rank test or Wilcoxon), and the Cox regression model

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.The use of SAS for data management and statistical modeling is illustrated using various examples.� Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics.

This practical book is primarily intended for health researchers with a basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis).� The use of examples from clinical trials and epidemiological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential SAS commands.

Presents the use of SAS software in the statistical approach for the management of data modeling Includes elements of the language and descriptive statistics Supplies measures of association, comparison of means, and proportions for two or more samples Explores linear and logistic regression Provides survival data analysis

This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research. The use of Stata for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epideomological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential Stata packages and commands.

Provides detailed examples of the use of Stata for common biostatistical tasks in medical research Features a work program structured around the four previous chapters and a series of practical exercises with commented corrections Includes an appendix to help the reader familiarize themselves with additional packages and commands Focuses on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data

Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

The family of statistical models known as Rasch models started with a simple model for responses to questions in educational tests presented together with a number of related models that the Danish mathematician Georg Rasch referred to as models for measurement. Since the beginning of the 1950s the use of Rasch models has grown and has spread from education to the measurement of health status. This book contains a comprehensive overview of the statistical theory of Rasch models.Part 1 contains the probabilistic definition of Rasch models, Part 2 describes the estimation of item and person parameters, Part 3 concerns the assessment of the data-model fit of Rasch models, Part 4 contains applications of Rasch models, Part 5 discusses how to develop health-related instruments for Rasch models, and Part 6 describes how to perform Rasch analysis and document results.