Meta-analysis of Binary Data Using Profile Likelihood

by Bohning; Dankmar

• ISBN13: 9781584886303
• ISBN10: 1584886307
• eBook ISBN(s): 9781040207130
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-03-27
• Publisher: Chapman & Hall/
• More Book Details

Summary

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 Likelihoodfocuses 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 quantifyingheterogeneity 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.

Table of Contents

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