© 2008 – Chapman and Hall/CRC
208 pages | 54 B/W Illus.
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.
"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
The occurrence of meta-analytic studies with binary outcome
Meta-analytic and multicenter studies
Center or study effect
Some examples of MAIPDs
Choice of effect measure
The Basic Model
Estimation of relative risk in meta-analytic studies using the profile likelihood
The profile likelihood under effect homogeneity
Reliable construction of the profile MLE
A fast converging sequence
Inference under effect homogeneity
Modeling Unobserved Heterogeneity
Unobserved covariate and the marginal profile likelihood
Concavity, the gradient function, and the PNMLE
The PNMLE via the EM algorithm
The EMGFU for the profile likelihood mixture
Likelihood ratio testing and model evaluation
Classification of centers
A reanalysis on the effect of beta-blocker after myocardial infarction
Modeling Covariate Information
Profile likelihood method
Applications of the model
Approximate likelihood model
Comparing profile and approximate likelihood
Analysis for the MAIPD on selective tract decontamination
Discussion of this comparison
Binomial profile likelihood
Incorporating Covariate Information and Unobserved Heterogeneity
The model for observed and unobserved covariates
Application of the model
Simplification of the model for observed and unobserved covariates
Working with CAMAP
Getting started with CAMAP
Analysis of modeling
Estimation of Odds Ratio Using the Profile Likelihood
Profile likelihood under effect homogeneity
Modeling covariate information
Quantification of Heterogeneity in an MAIPD
The profile likelihood as binomial likelihood
The unconditional variance and its estimation
Testing for heterogeneity in an MAIPD
An analysis of the amount of heterogeneity in MAIPDs: a case study
A simulation study comparing the new estimate and the DerSimonian–Laird estimate of heterogeneity variance
Scrapie in Europe: A Multicountry Surveillance Study as an MAIPD
The data on scrapie surveillance without covariates
Analysis and results
The data with covariate information on representativeness
Derivatives of the binomial profile likelihood
The lower bound procedure for an objective function with a bounded Hesse matrix
Connection between the profile likelihood odds ratio estimation and the Mantel–Haenszel estimator