Meta analysis in Clinical Research Principles and Procedures 1st Edition by Anoop Kumar ISBN 9789819923694 9819923697

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Product details:
ISBN 10: 9819923697
ISBN 13: 9789819923694
Author: Anoop Kumar

Meta analysis in Clinical Research Principles and Procedures 1st Edition Table of contents:

1: Introduction
1.1 Introduction
1.2 Need of Meta-Analysis
1.3 Popularity of Meta-Analysis
1.4 Guidelines and Checklists
1.5 Steps Involved
1.6 Available Software
1.7 Conclusion
References
2: Systematic Literature Review (SLR)
2.1 Introduction
2.2 Important Points
2.3 Importance of SLR
2.4 Difference Between Narrative and Systematic Literature Review (SLR)
2.5 Protocol Development
2.6 Steps to Perform SLR
2.6.1 Frame Your Objective and Research Questions
2.6.2 Define Eligibility Criteria
2.6.3 Search Strategy
2.6.4 Sorting of Studies
2.6.5 Quality Assessment
2.6.6 Collection of Data
2.6.7 Analysis
2.7 Conclusion
References
3: Quality Assessment of Studies
3.1 Introduction
3.2 Checklists/Scales
3.2.1 Assessing the Methodological Quality of Systematic Reviews (AMSTAR 2)
3.2.2 Risk of Bias in Systematic Reviews (ROBIS)
3.2.3 Centre for Evidence-Based Medicine (CEBM)
3.2.4 Cochrane Risk-of-Bias (RoB 2) Tool
3.2.5 Critical Appraisal Skills Programme (CASP)
3.2.6 Joanna Briggs Institute (JBI) Checklists
3.2.7 Newcastle-Ottawa Scale (NOS)
3.2.8 Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool
3.2.9 Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)
3.2.10 Jadad Scale
3.2.11 van Tulder Scale
3.2.12 CCRBT
3.2.13 GRADE
3.2.14 Avoiding Bias in Selecting Studies (AHRQ)
3.2.15 Database of Abstracts of Reviews of Effects (DARE)
3.2.16 Downs and Black Checklist
3.2.17 GRACE Checklists
3.3 Methodological Index for Non-randomised Studies (MINORS)
3.4 Conclusion
References
4: Extraction and Analysis of Data
4.1 Introduction
4.2 Extraction of Data
4.3 Analysis of Data
4.3.1 Weightage to Studies
4.3.2 Selection of Model
4.3.3 Choose an Effect Size
4.3.4 Mean Difference (MD) vs Standardised Mean Difference (SMD)
4.3.5 Response Ratios
4.3.5.1 Effect Sizes Based on Binary Data
4.3.5.1.1 Risk Ratio
4.3.5.1.2 Odds Ratio
4.3.5.1.3 Risk Difference
4.4 Selection of Effect Sizes (Risk Ratio, Odds Ratio, and Risk Difference)
4.4.1 Effect Sizes Based on Correlations
4.4.2 Converting Among Effect Sizes
4.4.3 Calculation of Heterogeneity
4.5 Conclusion
References
5: Models
5.1 Introduction
5.2 How Will the Selection of a Model Influence the Overall Effect Size?
5.3 Fixed Effect Model
5.4 Random Effect Model
5.5 Confidence Interval
5.6 Which Model Should We Use?
5.7 Conclusion
References
6: Heterogeneity and Publication Bias
6.1 Introduction
6.2 How to Identify and Measure Heterogeneity?
6.2.1 Eyeball Test
6.2.2 Chi-Squared (χ2) Test
6.2.3 I2
6.2.4 Cochran ́s Q Test
6.3 How to Deal with Heterogeneity?
6.4 Publication Bias
6.4.1 Assessment of Publication Bias
6.4.1.1 The Funnel Plot
6.4.1.2 Tests for Assessing Funnel Plot Asymmetry
6.4.1.2.1 The Begg ́s Rank Test
6.4.1.2.2 The Egger ́s Test
6.5 How to Avoid Publication Bias?
6.5.1 Prospective Registration
6.5.2 Search for Unpublished Results
6.5.3 Improve Publication Guidelines
6.6 Conclusion
References
7: Bias in Meta-Analysis
7.1 Introduction
7.2 Publication Bias
7.3 If the Search Is Internet Based (For Example, Medline)
7.3.1 Indexing Bias
7.3.2 Search Bias
7.3.3 Reference Bias
7.3.4 Multiple Publication Bias
7.3.5 Multiply Used Subjects Bias
7.3.5.1 Avoidance
7.4 Selection Bias
7.4.1 Inclusion Criteria Bias
7.4.2 Selector Bias
7.5 Within Study Bias
7.5.1 Bias Caused by the Meta-Analyst
7.5.2 Bias Due to Inadequate Accuracy in Reporting the Results by the Authors of the Studies
7.6 Other Biases
7.7 Conclusion
References
8: Sensitivity and Subgroup Analysis
8.1 Introduction
8.2 Subgroup Analysis
8.3 How to Interpret Subgroup Analyses?
8.4 Sensitivity Analysis
8.5 Sensitivity Analyses Vs. Subgroup Analysis
8.6 Conclusion
References
9: Meta-Regression
9.1 Introduction
9.2 Meta-Regression Approaches
9.3 Software
9.3.1 MetaXL 5.3
9.3.2 Meta-Regression in R
9.3.3 Statistica
9.4 Limitations
9.5 Applications
9.6 Conclusion
References
10: Plots
10.1 Introduction
10.2 How to Read a Forest Plot?
10.2.1 p-Values
10.2.2 Diamond Shape
10.2.2.1 p-Value
10.3 Funnel Plot
10.4 Conclusion
References
11: Network Meta-Analysis
11.1 Introduction
11.2 Network
11.3 Network Meta-Analysis
11.4 Direct and Indirect Evidence in a Treatment Network
11.5 Benefits
11.6 Network Meta-Analysis Models
11.7 Network Meta-Regression
11.8 Assumptions
11.8.1 Homogeneity of Direct Evidence
11.8.2 Transitivity
11.8.3 Consistency (Indirect and Direct)
11.9 Limitations
11.10 Conclusion
References
12: Registration and Software
12.1 Introduction
12.2 Why Is a Registry Needed?
12.3 When to Register?
12.4 Some Registry Sites
12.5 Steps Involved
12.6 Software
12.6.1 RevMan
12.6.1.1 Steps
12.6.2 Comprehensive Meta-Analysis (CMA)
12.6.3 MetaWin 2.0 and PhyloMeta
12.6.4 MetaAnalyst
12.6.5 Stata
12.6.6 R Packages
12.6.7 SPSS
12.6.8 Meta-Essentials (Excel Workbook)
12.6.9 MetaEasy (Excel Add-on)
12.6.10 MetaGenyo
12.6.11 StatsDirect
12.7 Conclusion
References
13: Common Mistakes
13.1 Introduction
13.2 Common Mistakes in Meta-Analysis
13.2.1 Data Entry Errors/Transposition Errors
13.2.2 Search Strategy
13.2.3 Flow Diagrams
13.2.4 Quality Assessment
13.2.5 Adequate Number of Studies
13.2.6 Forest Plot
13.2.7 Selection of Effect Sizes
13.2.8 Selection of Model
13.2.9 Interpretations of Results
13.2.10 Other Issues
13.3 Conclusion
References
14: Challenges
14.1 Introduction
14.2 Challenges
14.3 Criticism in Meta-Analysis
14.4 Limitations
14.5 Conclusion
References
15: Future Perspectives
15.1 Introduction
15.2 Future Perspectives
15.3 Conclusion

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Tags: Anoop Kumar, Meta analysis, Clinical Research, Procedures