| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046116798 | |
| 005 | 20220531124923 | |
| 008 | 220525s2022 flua b 001 0 eng | |
| 010 | ▼a 2021017096 | |
| 020 | ▼a 9780367610074 (hardback) | |
| 020 | ▼a 9780367619770 (paperback) | |
| 020 | ▼z 9781003107347 (ebook) | |
| 035 | ▼a (KERIS)BIB000016192957 | |
| 040 | ▼a 211029 ▼c 211029 ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a R853.M48 ▼b H37 2022 |
| 082 | 0 0 | ▼a 610.727 ▼2 23 |
| 084 | ▼a 610.727 ▼2 DDCK | |
| 090 | ▼a 610.727 ▼b D657 | |
| 245 | 0 0 | ▼a Doing meta-analysis with R : ▼b a hands-on guide / ▼c Mathias Harrer ... [et al.]. |
| 260 | ▼a Boca Raton : ▼b CRC Press/Taylor ＆ Francis Group, ▼c 2022. | |
| 300 | ▼a xxvi, 474 p. : ▼b ill. ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a "This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian meta-analysis approaches, SEM meta-analysis are also covered. A companion R package, dmetar, is introduced in the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Meta-analysis. |
| 650 | 0 | ▼a R (Computer program language). |
| 700 | 1 | ▼a Harrer, Mathias. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고7층/ | 청구기호 610.727 D657 | 등록번호 111865085 (5회 대출) | 도서상태 대출중 | 반납예정일 2026-01-28 | 예약 예약가능 | 서비스 |
컨텐츠정보
책소개
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide.
The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.
Features
- Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises
- Describes statistical concepts clearly and concisely before applying them in R
- Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.
정보제공 :
목차
1. Introduction. 2.Discovering R. 3. Effect Sizes. 4. Pooling Effect Sizes. 5. Between-Study Heterogeneity. 6. Forest Plots. 7. Subgroup Analyses. 8. Meta-Regression. 9. Publication Bias. 10. "Multilevel" Meta-Analysis. 11. Structural Equation Modeling Meta-Analysis. 12. Network Meta-Analysis. 13. Bayesian Meta-Analysis. 14. Power Analysis. 15. Risk of Bias Plots. 16. Reporting & Reproducibility. 17. Effect Size Calculation & Conversion.