CiNii 図書 - Applied meta-analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. -Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What's New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

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

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Applied meta-analysis with R and Stata

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