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Meta-Analysis: Statistics for Biology and Health

Autor Shahjahan Khan
en Limba Engleză Hardback – 28 oct 2020
This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided.
All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias.
The main attraction for readers is the book’s simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results.
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Specificații

ISBN-13: 9789811550317
ISBN-10: 981155031X
Pagini: 308
Ilustrații: XIV, 293 p. 79 illus., 66 illus. in color.
Dimensiuni: 160 x 241 x 23 mm
Greutate: 0.58 kg
Ediția:1st ed. 2020
Editura: Springer
Colecția Statistics for Biology and Health
Seria Statistics for Biology and Health

Locul publicării:Singapore, Singapore

Cuprins

Chapter 1. Introduction to meta-analysis.- Chapter 2. Ratio measures.- Chapter 3. One proportion.- Chapter 4. Risk difference (Two proportions).- Chapter 5. Weighted mean difference.- Chapter 6. Standardized mean difference.- Chapter 7. Correlation coefficient.- Chapter 8. Meta-regression.- Chapter 9. Network meta-analysis.- Chapter 10. Publication bias.


Recenzii

“The book will be a perfect guide to the interested undergraduate student with an aptitude for learning about evidence-based approach. Because each chapter offers application-based exercises with real data, there is no doubt that the book will capture interest of its readers from different fields and will provide hands-on experience with meta-analysis.” (Tugba Akkaya-Hocagil, Biometrics, Vol. 78 (1), March, 2022)

Notă biografică

Professor Dr. Shahjahan Khan is an eminent statistician and Emeritus Professor at the University of Southern Queensland (UniSQ), Australia. With a career spanning over 40 years in Australia, Bangladesh, Canada, Saudi Arabia, Oman, Bahrain, he held various esteemed positions, including Founding Professor of Statistics. Dr. Khan is a renowned expert in systematic review and meta-analysis, having conducted workshops and published extensively in these areas. He is a elected Expatriate Fellow of Bangladesh Academy of Sciences. He received several prestigious awards for his research and leadership, including gold medals from ISESCO and ISOSS. As the President of ISOSS, he was the main organizer of successful international conferences in Malaysia, Egypt, Qatar, Indonesia, Bangladesh and Pakistan. Since 2022, he has been serving as the Vice Chancellor of Asian University of Bangladesh. Dr. Khan's exceptional academic excellence, research contributions, and international reputation have cemented his position as a distinguished figure in the field of statistics and health sciences. Dr. Md Shafiur Rahman is a Junior Associate Professor at the School of Health Innovation, Kanagawa University of Human Services, Japan. He is also affiliated with the Research Center for Child Mental Development and Division of Epidemiology and Biostatistics at Hamamatsu University School of Medicine, and the National Cancer Center, Japan. Dr. Rahman has served as a data scientist for the Asia Cohort Consortium for nearly eight years and was previously an adjunct faculty member at St Luke's International University, Japan. Dr. Rahman's research interests lie in Bayesian statistics, epidemiology, child neurodevelopment, universal health coverage assessment, and systematic review and meta-analysis. He has published extensively in prestigious medical journals and received notable fellowships and research grants for his contributions to the field.

Textul de pe ultima copertă

This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided.
All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias.
The main attraction for readers is the book’s simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results.

Caracteristici

Explains how to perform meta-analysis using the easy-to-use statistical package MetaXL, a free add-on to MS Excel Includes lesser-known models such as inverse variance heterogeneity and quality effect Allows the reader to understand the statistical reasoning and methods behind meta-analysis, within the framework of systematic review and evidence-based decision-making Makes it easy to compare results of meta-analyses based on different models