Data Analysis of Medical Studies - Reading and Reporting

This book is written for the many health professionals who are regularly frustrated by elegant but ambiguous descriptions of results of data analysis. It uses articles of the New England Journal of Medicine to demonstrate how ambiguous descriptions of results of data analysis may be read, so that it is clear what they do and do not reveal. These demonstrations also show how statistics is misused.

Sommario

Chapter 1. Data Distribution.- Chapter 2. Graphical Displays of Data Distributions.- Chapter 3. Descriptive Statistics.- Chapter 4. Unpaired Qualitative Data Sets.- Chapter 5. Paired Qualitative Data Sets.- Chapter 6. Unpaired Quantitative Data Sets.- Chapter 7. Paired Quantitative Data Sets.- Chapter 8. Association Between Two Qualitative Variables.- Chapter 9. Relationship Between Two Quantitative Variables.

Info autore

Potter C. Chang is Emeritus Professor of Biostatistics at the Department of Biostatistics, School of Public Health, UCLA.

Riassunto

This book is written for the many health professionals who are regularly frustrated by elegant but ambiguous descriptions of results of data analysis. It uses articles of the New England Journal of Medicine to demonstrate how ambiguous descriptions of results of data analysis may be read, so that it is clear what they do and do not reveal. These demonstrations also show how statistics is misused.

Dettagli sul prodotto

• Autori: Potter C Chang, Potter C. Chang
• Editore: Springer, Berlin
• Lingue: Inglese, Tedesco
• Formato: Copertina rigida
• Pubblicazione: 15.03.2024
• EAN: 9783031499838
• ISBN: 978-3-0-3149983-8
• Pagine: 401
• Illustrazioni: XIII, 401 p. 88 illus., 22 illus. in color.
• Categorie: Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica matematica; Scienze sociali, diritto, economia > Media, comunicazione > Scienze della comunicazione; Data Mining, Statistics, Wissensbasierte Systeme, Expertensysteme, Data Mining and Knowledge Discovery, Biostatistics, oversimplification, adjusted, misleading, statistical significance, informative reporting, Intelligent reading, hazard ratio, confidence interval, subgroup analysis, revelations of data, paired data sets