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＜電子ブック＞
Meta-Analysis : Methods for Health and Experimental Studies / by Shahjahan Khan
(Statistics for Biology and Health. ISSN:21975671)

版	1st ed. 2020.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XIV, 293 p. 79 illus., 66 illus. in color : online resource
著者標目	*Khan, Shahjahan author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Research -- Methodology  全ての件名で検索 LCSH:Sociology -- Methodology  全ての件名で検索 LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Biostatistics FREE:Research Skills FREE:Sociological Methods FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-981-15-5032-4

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