Meta-analysis of Binary Data Using Profile Likelihood

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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.

Author(s): Dankmar Bohning, Sasivimol Rattanasiri, Ronny Kuhnert
Series: Chapman & Hall/CRC Interdisciplinary Statistics
Edition: 1
Publisher: Chapman and Hall/CRC
Year: 2008

Language: English
Pages: 207

Title......Page 6
Copyright......Page 7
Contents......Page 8
Preface......Page 12
Abbreviations......Page 16
CHAPTER 1: Introduction......Page 18
CHAPTER 2: The basic model......Page 40
CHAPTER 3: Modeling unobserved heterogeneity......Page 58
CHAPTER 4: Modeling covariate information......Page 72
CHAPTER 5: Alternative approaches......Page 92
CHAPTER 6: Incorporating covariate information and unobserved heterogeneity......Page 110
CHAPTER 7: Working with CAMAP......Page 122
CHAPTER 8: Estimation of odds ratio using the profile likelihood......Page 140
CHAPTER 9: Quantification of heterogeneity in a MAIPD......Page 148
CHAPTER 10: Scrapie in Europe: a multicountry surveillance study as a MAIPD......Page 166
APPENDIX A......Page 186
Bibliography......Page 192
Author index......Page 200
Subject index......Page 203