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.
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