Ding-Geng Chen (Din) (USA University of North Carolina), Karl E. Peace (USA Georgia Southern University)
Applied Meta-Analysis with R and Stata
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Ding-Geng Chen (Din) (USA University of North Carolina), Karl E. Peace (USA Georgia Southern University)
Applied Meta-Analysis with R and Stata
- Broschiertes Buch
In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.
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In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.
Produktdetails
- Produktdetails
- Chapman & Hall/CRC Biostatistics Series
- Verlag: Taylor & Francis Ltd
- 2 ed
- Seitenzahl: 424
- Erscheinungstermin: 26. September 2022
- Englisch
- Abmessung: 156mm x 235mm x 29mm
- Gewicht: 684g
- ISBN-13: 9780367709341
- ISBN-10: 0367709341
- Artikelnr.: 65614037
- Chapman & Hall/CRC Biostatistics Series
- Verlag: Taylor & Francis Ltd
- 2 ed
- Seitenzahl: 424
- Erscheinungstermin: 26. September 2022
- Englisch
- Abmessung: 156mm x 235mm x 29mm
- Gewicht: 684g
- ISBN-13: 9780367709341
- ISBN-10: 0367709341
- Artikelnr.: 65614037
Ding-Geng (Din) Chen is a fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina-Chapel Hill, USA. Formerly, he was a Professor of Biostatistics at the University of Rochester, New York, USA, the Karl E. Peace Endowed Eminent Scholar Chair and professor in Biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University, USA, and a professor of statistics at South Dakota Stata University, USA. Dr. Chen's research interests include clinical trial biostatistical methodological development in Bayesian models, survival analysis, multi-level modelling and longitudinal data analysis, and statistical meta-analysis. He has published more than 200 refereed papers and co-authored/co-edited 30 book in statistics. Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar, Founding Director of the Center for Biostatistics, Professor of Biostatistics, and Senior Research Scientist in the Jiann-Ping Hsu College of Public Health at Georgia Southern University (GSU). Dr. Peace has made pivotal contributions in the development and approval of drugs to treat numerous diseases and disorders. A fellow of the ASA, he has been a recipient of many honors, including the Drug Information Association Outstanding Service Award, the American Public Health Association Statistics Section Award, The First recipient of the President's Medal for outstanding contributions to GSU, and recognition by the Georgia and US Houses of Representatives, and the Virginia House of Delegates.
1. Introduction to R and Stata for Meta
Analysis 2. Research Protocol for Meta
Analyses 3. Fixed
E ects and Random
E ects in Meta
Analysis 4. Meta
Analysis with Binary Data 5. Meta
Analysis for Continuous Data 6. Heterogeneity in Meta
Analysis 7. Meta
Regression 8. Multivariate Meta
Analysis 9. Publication Bias in Meta
Analysis 10. Strategies to Handle Missing Data in Meta
Analysis 11. Meta
Analysis for Evaluating Diagnostic Accuracy 12. Network Meta
Analysis 13. Meta
Analysis for Rare Events 14. Meta
Analyses with Individual Patient
Level Data versus Summary Statistics 15. Other R/Stata Packages for Meta
Analysis
Analysis 2. Research Protocol for Meta
Analyses 3. Fixed
E ects and Random
E ects in Meta
Analysis 4. Meta
Analysis with Binary Data 5. Meta
Analysis for Continuous Data 6. Heterogeneity in Meta
Analysis 7. Meta
Regression 8. Multivariate Meta
Analysis 9. Publication Bias in Meta
Analysis 10. Strategies to Handle Missing Data in Meta
Analysis 11. Meta
Analysis for Evaluating Diagnostic Accuracy 12. Network Meta
Analysis 13. Meta
Analysis for Rare Events 14. Meta
Analyses with Individual Patient
Level Data versus Summary Statistics 15. Other R/Stata Packages for Meta
Analysis
1. Introduction to R and Stata for Meta
Analysis 2. Research Protocol for Meta
Analyses 3. Fixed
E ects and Random
E ects in Meta
Analysis 4. Meta
Analysis with Binary Data 5. Meta
Analysis for Continuous Data 6. Heterogeneity in Meta
Analysis 7. Meta
Regression 8. Multivariate Meta
Analysis 9. Publication Bias in Meta
Analysis 10. Strategies to Handle Missing Data in Meta
Analysis 11. Meta
Analysis for Evaluating Diagnostic Accuracy 12. Network Meta
Analysis 13. Meta
Analysis for Rare Events 14. Meta
Analyses with Individual Patient
Level Data versus Summary Statistics 15. Other R/Stata Packages for Meta
Analysis
Analysis 2. Research Protocol for Meta
Analyses 3. Fixed
E ects and Random
E ects in Meta
Analysis 4. Meta
Analysis with Binary Data 5. Meta
Analysis for Continuous Data 6. Heterogeneity in Meta
Analysis 7. Meta
Regression 8. Multivariate Meta
Analysis 9. Publication Bias in Meta
Analysis 10. Strategies to Handle Missing Data in Meta
Analysis 11. Meta
Analysis for Evaluating Diagnostic Accuracy 12. Network Meta
Analysis 13. Meta
Analysis for Rare Events 14. Meta
Analyses with Individual Patient
Level Data versus Summary Statistics 15. Other R/Stata Packages for Meta
Analysis