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El. knyga: Modern Meta-Analysis: Review and Update of Methodologies

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  • Formatas: PDF+DRM
  • Išleidimo metai: 02-May-2017
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319558950
Kitos knygos pagal šią temą:
Modern Meta-Analysis: Review and Update of MethodologiesDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 02-May-2017
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319558950
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Modern meta-analyses do more than combine the effect sizes of a series of similar studies. Meta-analyses are currently increasingly applied for any analysis beyond the primary analysis of studies, and for the analysis of big data. This 26-chapter book was written for nonmathematical professionals of medical and health care, in the first place, but, in addition, for anyone involved in any field involving scientific research. The authors have published over twenty innovative meta-analyses from the turn of the century till now. This edition will review the current state of the art, and will use for that purpose the methodological aspects of the authors' own publications, in addition to other relevant methodological issues from the literature.

Are there alternative works in the field? Yes, there are, particularly in the field of psychology. Psychologists have invented meta-analyses in 1970, and have continuously updated methodologies. Although very interesting, their work, just like the whole discipline of psychology, is rather explorative in nature, and so is their focus to meta-analysis. Then, there is the field of epidemiologists. Many of them are from the school of angry young men, who publish shocking news all the time, and JAMA and other publishers are happy to publish it. The reality is, of course, that things are usually not as bad as they seem. Finally, some textbooks, written by professional statisticians, tend to use software programs with miserable menu programs and requiring lots of syntax to be learnt. This is prohibitive to clinical and other health professionals.

 





The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research. 
1 Meta-analysis in a Nutshell
1(22)
1.1 Introduction
1(2)
1.2 Objectives
3(2)
1.3 How to Perform a Meta-analysis?
5(3)
1.4 Scientific Rigor, Rule 1
8(2)
1.5 Scientific Rigor, Rule 2
10(1)
1.6 Scientific Rigor, Rule 3
11(1)
1.7 Scientific Rigor, Rule 4
12(3)
1.8 First Pitfall
15(1)
1.9 Second Pitfall
16(3)
1.10 Third Pitfall
19(1)
1.11 Benefits and Criticisms of Meta-analyses
19(2)
1.12 Conclusion
21(2)
Reference
22(1)
2 Mathematical Framework
23(20)
2.1 Introduction
23(1)
2.2 General Framework
24(1)
2.3 Continuous Outcome Data, Mean and Standard Deviation
25(1)
2.3.1 Means and Standard Deviation (SD)
25(1)
2.4 Continuous Outcome Data, Strictly Standardized Mean Difference (SSMD)
26(1)
2.5 Continuous Outcome Data, Regression Coefficient and Standard Error
27(1)
2.6 Continuous Outcome Data, Student's T-Value
28(1)
2.7 Continuous Outcome Data, Correlation Coefficient (R or r) and Its Standard Error
29(2)
2.8 Continuous Outcome Data, Coefficient of Determination R2 or r2 and Its Standard Error
31(1)
2.9 Binary Outcome Data, Risk Difference
32(1)
2.10 Binary Outcome Data, Relative Risk
32(1)
2.11 Binary Outcome Data, Odds Ratio
33(1)
2.12 Binary Outcome Data, Survival Data
33(1)
2.13 Pitfalls, Publication Bias
34(1)
2.14 Pitfalls, Heterogeneity
35(3)
2.15 Pitfalls, Lack of Sensitivity
38(1)
2.16 New Developments
39(1)
2.17 Conclusions
40(3)
Reference
41(2)
3 Meta-analysis and the Scientific Method
43(8)
3.1 Introduction
43(1)
3.2 Example 1, the Potassium Meta-analysis of the Chap. 6
44(1)
3.3 Example 2, the Calcium Channel Blocker Meta-analysis of the Chap. 6
45(1)
3.4 Example 3, the Large Randomized Trials Meta-analyses of the Chap. 6
45(1)
3.5 Example 4, the Diabetes and Heart Failure Meta-analysis of the Chap. 7
46(1)
3.6 Example 5, the Adverse Drug Effect Admissions and the Type of Research Group Meta-analysis of the Chap. 8
46(1)
3.7 Example 6, the Coronary Events and Collaterals Meta-analysis of the Chap. 8
47(1)
3.8 Example 7, the Diagnostic Meta-analysis of Metastatic Lymph Node Imaging of the Chap. 10
47(1)
3.9 Example 8, the Homocysteine and Cardiac Risk Meta-analysis of the Chap. 11
48(1)
3.10 Conclusions
48(3)
References
49(2)
4 Meta-analysis and Random Effect Analysis
51(12)
4.1 Introduction
51(2)
4.2 Visualizing Heterogeneity
53(2)
4.3 Binary Outcome Data, Fixed Effect Analysis
55(1)
4.4 Binary Outcome Data, Random Effect Analysis
56(2)
4.5 Continuous Outcome Data, Fixed Effect Analysis
58(2)
4.6 Continuous Outcome Data, Random Effect Analysis
60(1)
4.7 Conclusions
61(2)
Reference
62(1)
5 Meta-analysis with Statistical Software
63(16)
5.1 Introduction
63(1)
5.2 Using Online Meta-analysis Calculators and MetaXL Free Meta-analysis Software
63(1)
5.3 Continuous Outcome Data, Online Meta-analysis Calculator
64(3)
5.4 Binary Outcome Data, MetaXL Free Meta-analysis Software
67(8)
5.4.1 Traditional Random Effect Analysis
68(4)
5.4.2 Quasi Likelihood (Invert Variance Heterogeneity (IVhet)) Modeling for Heterogeneity
72(3)
5.5 Conclusion
75(4)
Reference
77(2)
6 Meta-analyses of Randomized Controlled Trials
79(14)
6.1 Introduction
79(2)
6.2 Example 1: Single Outcomes
81(4)
6.3 Example 1, Confirming the Scientific Question
85(1)
6.4 Example 2: Multiple Outcomes
85(2)
6.5 Example 2, Handling Multiple Outcomes
87(1)
6.6 Example 3, Large Meta-analyses Without Need for Pitfall Assessment
88(2)
6.7 Conclusion
90(3)
Reference
91(2)
7 Meta-analysis of Observational Plus Randomized Studies
93(8)
7.1 Introduction and Example
93(1)
7.2 Sound Clinical Arguments and Scientific Question
94(1)
7.3 Summary Statistics
95(1)
7.4 Pooled Results
96(2)
7.5 Heterogeneity Assessments
98(1)
7.6 Publication Bias Assessments
98(1)
7.7 Robustness Assessments
99(1)
7.8 Improved Information from the Combined Meta-analysis
99(1)
7.9 Conclusion
100(1)
Reference
100(1)
8 Meta-analysis of Observational Studies
101(14)
8.1 Introduction
101(1)
8.2 Prospective Open Evaluation Studies
102(1)
8.3 Example 1, Event Analysis in Patients with Collateral Coronary Arteries
103(1)
8.4 Example 1, the Scientific Method
103(1)
8.5 Example 1, Publication Bias
104(1)
8.6 Example 1, Pooled Results, Tests for Heterogeneity and Robustness
104(2)
8.7 Example 1, Meta-regression Analysis
106(2)
8.8 Conclusions
108(1)
8.9 Example 2, Event Analysis of Iatrogenic Hospital Admissions
108(1)
8.10 Example 2, the Scientific Method
108(1)
8.11 Example 2, Publication Bias
109(1)
8.12 Example 2, Overall Results and Heterogeneity and Lack of Robustness
110(2)
8.13 Example 2, Meta-regression
112(1)
8.14 Example 2, Conclusions
113(1)
8.15 Conclusion
113(2)
Reference
114(1)
9 Meta-regression
115(12)
9.1 Introduction
115(1)
9.2 Example 1, Continuous Outcome
115(6)
9.2.1 Exploratory Purpose
116(1)
9.2.2 Confounding
117(2)
9.2.3 Interaction
119(2)
9.3 Example 2, Binary Outcome
121(3)
9.3.1 Exploratory Purpose
121(1)
9.3.2 Confounding
122(1)
9.3.3 Interaction
123(1)
9.4 Conclusion
124(3)
Reference
126(1)
10 Meta-analysis of Diagnostic Studies
127(8)
10.1 Introduction
127(2)
10.2 Diagnostic Odds Ratios (DORs)
129(1)
10.3 Example
129(3)
10.4 Constructing Summary ROC Curves
132(1)
10.5 Alternative Methods
133(1)
10.6 Conclusions
133(2)
References
134(1)
11 Meta-Meta-analysis
135(10)
11.1 Introduction
135(1)
11.2 Example 1, Meta-Meta-analysis for Re-assessment of the Pitfalls of the Original Meta-analyses
136(5)
11.3 Example 2, Meta-Meta-analysis for Meta-learning Purposes
141(1)
11.4 Conclusion
142(3)
Reference
143(2)
12 Network Meta-analysis
145(12)
12.1 Introduction
145(1)
12.2 Example 1, Lazarou-1 (JAMA 1998; 279: 1200-5)
146(2)
12.3 Example 2, Atiqi (Int J Clin Pharmacol Ther 2009; 47: 549-56)
148(3)
12.4 Example 3, Lazarou-1 and Atiqi (JAMA 1998; 279: 1200-5, and Int J Clin Pharmacol Ther 2009; 47: 549-56)
151(1)
12.5 Example 4, Lazarou-1 and -2 (JAMA 1998; 279: 1200-5)
152(3)
12.6 Conclusion
155(2)
Reference
155(2)
13 Random Intercepts Meta-analysis
157(10)
13.1 Introduction
157(2)
13.2 Example, Meta-analysis of Three Studies
159(6)
13.3 Conclusion
165(2)
Reference
165(2)
14 Probit Meta-regression
167(10)
14.1 Introduction
167(1)
14.2 Example
167(8)
14.3 Conclusion
175(2)
Reference
175(2)
15 Meta-analysis with General Loglinear Models
177(8)
15.1 Introduction
177(1)
15.2 Example, Weighted Multiple Linear Regression
177(3)
15.3 Example, General Loglinear Modeling
180(3)
15.4 Conclusion
183(2)
Reference
183(2)
16 Meta-analysis with Variance Components
185(10)
16.1 Introduction
185(1)
16.2 Example 1
185(2)
16.3 Example 2
187(2)
16.4 Example 3
189(3)
16.5 Conclusion
192(3)
Reference
193(2)
17 Ensembled Correlation Coefficients
195(10)
17.1 Introduction
195(1)
17.2 Ensemble Learning with SPSS Modeler
196(1)
17.3 Example
197(6)
17.4 Conclusion
203(2)
Reference
204(1)
18 Ensembled Accuracies
205(12)
18.1 Introduction
205(1)
18.2 Ensemble Learning with SPSS Modeler
206(1)
18.3 Example
207(8)
18.4 Conclusion
215(2)
Reference
216(1)
19 Multivariate Meta-analysis
217(16)
19.1 Introduction
217(1)
19.2 Example 1
218(4)
19.3 Example 2
222(4)
19.4 Example 3
226(5)
19.5 Conclusions
231(2)
Reference
231(2)
20 Transforming Odds Ratios into Correlation Coefficients
233(10)
20.1 Introduction
233(1)
20.2 Unweighted Odds Ratios as Effect Size Calculators
234(1)
20.3 Regression Coefficients and Correlation Coefficients as Replacement of Odds Ratios
234(4)
20.4 Examples of Approximation Methods for Computing Correlation Coefficients from Odds Ratios
238(1)
20.4.1 The Yule Approximation
238(1)
20.4.2 The Ulrich Approximation
239(1)
20.5 Computing Tetrachoric Correlation Coefficients from an Odds Ratio
239(2)
20.6 Conclusion
241(2)
Reference
242(1)
21 Meta-analyses with Direct and Indirect Comparisons
243(6)
21.1 Introduction
243(1)
21.2 Challenging the Exchangeability Assumption
244(1)
21.3 Frequentists' Methods for Indirect Comparisons
244(1)
21.4 The Confidence Intervals Methods for Indirect Comparisons
245(2)
21.5 Real Data Examples
247(1)
21.6 Conclusion
248(1)
Reference
248(1)
22 Contrast Coefficients Meta-analysis
249(12)
22.1 Introduction
249(1)
22.2 Example
250(2)
22.3 Fixed Effect Meta-analysis
252(1)
22.4 Random Effect Meta-analysis
253(1)
22.5 Principles of Linear Contrast Testing
254(1)
22.6 Null Hypothesis Testing of Linear Contrasts
255(1)
22.7 Pocket Calculator One-Way Analysis of Variance (ANOVA) of Linear Contrasts
256(1)
22.8 Contrast Testing Using One Way Analysis of Variance on SPSS Statistical Software
257(1)
22.9 Conclusion
258(3)
References
259(2)
23 Meta-analysis with Evolutionary Operations (EVOPs)
261(8)
23.1 Introduction
261(1)
23.2 Example of the Meta-analysis of Three Studies Assessing Determinants of Infectious Disease
262(1)
23.3 First Study
263(1)
23.4 Second Study
263(2)
23.5 Third Study
265(1)
23.6 Meta-analysis of the Above Three Studies
266(1)
23.7 Conclusion
267(2)
Reference
267(2)
24 Meta-analysis with Heterogeneity as Null-Hypothesis
269(10)
24.1 Introduction
269(1)
24.2 Heterogeneity Rather Than Homogeneity of Studies as Null-Hypothesis
270(2)
24.3 Frequency Distribution of the Treatments
272(1)
24.4 Beneficial Effects of the Treatments, Histograms
272(1)
24.5 Beneficial Effects of Treatments, Clusters
273(1)
24.6 Beneficial Effects of Treatments, Network of Causal Associations Displayed as a Web
274(1)
24.7 Beneficial Effects of Treatments, Decision Trees
275(1)
24.8 Beneficial Effects of Treatments, Accuracy Assessment of Decision Tree Output
276(1)
24.9 Conclusions
276(3)
Reference
277(2)
25 Meta-analytic Thinking and Other Spin-Offs of Meta-analysis
279(20)
25.1 Introduction
279(1)
25.2 Meta-learning
279(1)
25.3 Meta-analytic Graphing
280(2)
25.4 Meta-analytic Thinking in Writing Study Protocols and Reports
282(2)
25.5 Meta-analytic Forest Plots of Baseline Patient Characteristics
284(3)
25.6 Meta-analysis of Forest Plots of Propensity Scores
287(3)
25.7 Meta-analytic Thinking: Effect Size Assessments of Important Scientific Issues Other Than the Main Study Outcomes
290(2)
25.8 Forest Plots for Assessing and Adjusting Baseline Characteristic Imbalance
292(1)
25.9 Sensitivity Analysis
293(1)
25.10 Pooled Odds Ratios for Multidimensional Outcome Effects
294(2)
25.11 Ratios of Odds Ratios for Subgroup Analyses
296(2)
25.12 Conclusion
298(1)
Reference
298(1)
26 Novel Developments
299(10)
26.1 Introduction, Condensed Review of the Past
299(1)
26.2 Condensed Review of the Current Edition and Novel Developments
300(1)
26.3 Meta-analysis of Studies Tested with Analyses of Variance
301(2)
26.4 Meta-analyses of Crossover Trials with Binary Outcomes
303(1)
26.5 Equivalence Study Meta-analysis
304(2)
26.6 Agenda-Driven Meta-analyses
306(1)
26.7 Hills' Plurality of Reasoning Statement, Evidence Based Medicine Avant la Lettre
307(1)
26.8 Conclusion
307(2)
Reference
308(1)
Index 309
The authors are well-qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015), and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 17 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are convinced that the scientific method of statistical reasoning and hypothesis testing is little used by physicians and other health workers, and they hope that the current production will help them find the appropriate ways for answering their scientific questions.

Three textbooks complementary to the current production and written by the same authors are Statistics applied to clinical studies 5th edition, 2012, Machine learning in medicine a complete overview, 2015, SPSS for starters and 2nd levelers, 2015, all of them edited by Springer Heidelberg Germany.
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