Meta-analysis Of Binary Data Using Profile Likelihood (chapman & Hall/crc Interdisciplinary Statistics)
by Dankmar Bohning /
2008 / English / PDF
10.4 MB Download
Providing reliable information on an intervention effect,
meta-analysis is a powerful statistical tool for analyzing and
combining results from individual studies.
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
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.
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.
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.
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.