Description
Meta-analysis of Binary Data Using Profile Likelihood
Chapman & Hall/CRC Interdisciplinary Statistics Series
Authors: Bohning Dankmar, Rattanasiri Sasivimol, Kuhnert Ronny
Language: EnglishSubjects for Meta-analysis of Binary Data Using Profile Likelihood:
Keywords
Profile Likelihood; Log Relative Risk; Profile Log Likelihood; Profile Likelihood Approach; Nuisance Parameter; Unobserved Heterogeneity; Covariate Information; Profile Likelihood Method; Mantel Haenszel Estimate; Mantel Haenszel Estimator; Baseline Heterogeneity; Em Algorithm; NRT; Heterogeneity Variance; Generalized Linear Model; Maximum Likelihood Estimator; Nonparametric Maximum Likelihood Estimator; Newton Raphson Iteration; Binary Covariate; BIC Criterion; BCG Vaccine; Fixed Point Mapping; DerSimonian Laird Estimator; Crude Risk Ratio; Fixed Point Procedure
74.82 €
In Print (Delivery period: 14 days).
Add to cart the book of Bohning Dankmar, Rattanasiri Sasivimol, Kuhnert RonnyPublication date: 10-2019
· 15.6x23.4 cm · Paperback
184.47 €
Subject to availability at the publisher.
Add to cart the book of Bohning Dankmar, Rattanasiri Sasivimol, Kuhnert RonnyPublication date: 04-2008
224 p. · 15.6x23.4 cm · Hardback
Description
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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.