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E-raamat: Meta-analysis of Binary Data Using Profile Likelihood

, , (University of Southampton, United Kingdom)
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Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling of a meta-analysis with individually pooled data (MAIPD). It presents a unifying approach to modeling a treatment effect in a meta-analysis of clinical trials with binary outcomes.

After illustrating the meta-analytic situation of an MAIPD with several examples, the authors introduce the profile likelihood model and extend it to cope with unobserved heterogeneity. They describe elements of log-linear modeling, ways for finding the profile maximum likelihood estimator, and alternative approaches to the profile likelihood method. The authors also discuss how to model covariate information and unobserved heterogeneity simultaneously and use the profile likelihood method to estimate odds ratios. The final chapters look at quantifying heterogeneity in an MAIPD and show how meta-analysis can be applied to the surveillance of scrapie.

Containing new developments not available in the current literature, along with easy-to-follow inferences and algorithms, this book enables clinicians to efficiently analyze MAIPDs.

Arvustused

"The book is very focused on the methods the authors have developed for meta-analysis. It includes a lot of technical details for solving likelihood equations. The advanced key method is based on nonparametric mixing distributions and the question how many mixing components do we have is the crucial one. ... A positive aspect is the development of the software tool. The software CAMAP can be downloaded with no costs from the website: http://www.personal.rdg.ac.uk/~sns05dab/Software.html ..." -ISCB News #49, June 2010 "The authors have succeeded in demonstrating recent developments and the utility of statistical tools for MAIPD-type meta-analysis. ... a strong background in mathematics is not needed. The material that is covered in this book can be a part of an advanced biostatistics course. The book should be accessible and useful to graduate students in biostatistics and biostatisticians working in theory as well as in applied areas. The book is well worth recommending for purchase by a library." -Journal of the Royal Statistical Society, Series A, 2010, 173 "I enjoyed reading this book. Having worked with commonly used tools of meta-analysis, I learned a new set of tools and options. The writing is clear and easy to follow. ... this is a good book that assumes only a basic knowledge of metaanalysis. Students new to the subject should find it easy to follow while old hands will find interesting new research areas. I would recommend it to anyone interested in the field." -Rafael Perera, Journal of the American Statistical Association, June 2010 "I recommend the book as a supplement for a graduate-level course in meta-analysis and for readers seeking an alternative approach to analyze MAIPD or multicenter clinical trial studies, specifically when the outcome variable is the occurrence of rare events." -Taye H. Hamza, Statistics in Medicine, 2009 "The text contains many real-world examples which add to the usefulness of the book. ... The balance between statistical theory and practical applications with CAMAP make the text suitable for private study and research." -C.M. O'Brien, International Statistical Review, 2009 "I am not aware of a more complete source for this topic. The authors' presentation of the core ideas behind the derivation and use of PML estimates is accessible to anyone familiar with standard likelihood-based estimation. The many good examples facilitate intelligent application of these ideas, and the described software makes implementation simple." -Eloise Kaizar, Biometrics, June 2009

Preface xi
Abbreviations xv
Introduction
1(22)
The occurrence of meta-analytic studies with binary outcome
1(6)
Meta-analytic and multicenter studies
7(2)
Center or study effect
9(1)
Sparsity
10(2)
Some examples of MAIPDs
12(2)
Choice of effect measure
14(9)
The basic model
23(18)
Likelihood
23(1)
Estimation of relative risk in meta-analytic studies using the profile likelihood
24(1)
The profile likelihood under effect homogeneity
25(3)
Reliable construction of the profile MLE
28(1)
A fast converging sequence
29(4)
Inference under effect homogeneity
33(8)
Modeling unobserved heterogeneity
41(14)
Unobserved covariate and the marginal profile likelihood
42(1)
Concavity, the gradient function and the PNMLE
43(2)
The PNMLE via the EM algorithm
45(1)
The EMGFU for the profile likelihood mixture
46(1)
Likelihood ratio testing and model evaluation
47(1)
Classification of centers
48(1)
A reanalysis on the effect of beta-blocker after myocardial infarction
48(7)
Modeling covariate information
55(20)
Classical methods
55(4)
Profile likelihood method
59(3)
Applications of the model
62(12)
Summary
74(1)
Alternative approaches
75(18)
Approximate likelihood model
75(1)
Multilevel model
76(1)
Comparing profile and approximate likelihood
77(3)
Analysis for the MAIPD on selective tract decontamination
80(2)
Simulation study
82(3)
Discussion of this comparison
85(2)
Binomial profile likelihood
87(6)
Incorporating covariate information and unobserved heterogeneity
93(12)
The model for observed and unobserved covariates
93(7)
Application of the model
100(2)
Simplification of the model for observed and unobserved covariates
102(3)
Working with CAMAP
105(18)
Getting started with CAMAP
106(5)
Analysis of modeling
111(10)
Conclusion
121(2)
Estimation of odds ratio using the profile likelihood
123(8)
Profile likelihood under effect homogeneity
124(2)
Modeling covariate information
126(5)
Quantification of heterogeneity in a MAIPD
131(18)
The problem
131(3)
The profile likelihood as binomial likelihood
134(1)
The unconditional variance and its estimation
134(6)
Testing for heterogeneity in a MAIPD
140(3)
An analysis of the amount of heterogeneity in MAIPDs: a case study
143(1)
A simulation study comparing the new estimate and the DerSimonian-Laird estimate of heterogeneity variance
144(5)
Scrapie in Europe: a multicountry surveillance study as a MAIPD
149(26)
The problem
149(2)
The data on scrapie surveillance without covariates
151(1)
Analysis and results
152(1)
The data with covariate information on representativeness
153(16)
A
A.1 Derivatives of the binomial profile likelihood
169(1)
A.2 The lower bound procedure for an objective function with a bounded Hesse matrix
170(2)
A.3 Connection between the profile likelihood odds ratio estimation and the Mantel-Haenszel estimator
172(3)
Bibliography 175(8)
Author index 183(3)
Subject index 186
Bohning, Dankmar; Rattanasiri, Sasivimol; Kuhnert, Ronny
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