- 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
Autor/Autorin: Christophe Lalanne
Christophe Lalanne is a Research Engineer at the Paris-Diderot University, France. His research involves the modeling of data from clinical research
Autor/Autorin: Mounir Mesbah
Mounir Mesbah is Head of Biostatistics in the Statistics Institute of the Pierre and Marie Curie University in Paris, France, where he is also a researcher at the Theoretical and Applied Statistics Laboratory. His research involves theoretical and applied statistics, particularly in the field of health and medicine.
Focusing on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data, this practical guide uses the statistics software Stata to survey all aspects of data processing and statistical analysis of health data, including full coverage of regression models commonly found in medical statistics.
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.
1. Language Elements.
2. Measures of Association, Comparisons of Means and Proportions.
3. Linear Regression.
4. Logistic Regression and Epidemiological Analyses.
5. Survival Data Analysis.
Christophe Lalanne is a Research Engineer at the Paris-Diderot University, France. His research involves the modeling of data from clinical research
Mounir Mesbah is Head of Biostatistics in the Statistics Institute of the Pierre and Marie Curie University in Paris, France, where he is also a researcher at the Theoretical and Applied Statistics Laboratory. His research involves theoretical and applied statistics, particularly in the field of health and medicine.
