Meta-analysis of Binary Data Using Profile Likelihood
Meta-analysis of Binary Data Using Profile Likelihood
复制标题
使用轮廓似然法对二进制数据进行元分析
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
S. Rattanasiri
中科院分区：
文献类型：
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作者：
D. Böhning;R. Kuhnert;S. Rattanasiri
简介 具有二元结果的荟萃分析研究的发生 荟萃分析和多中心研究 中心或研究效应 稀疏性 MAIPD 的一些示例 效应测量的选择 基本模型似然 使用轮廓似然估计荟萃分析研究中的相对风险 效应同质性下的轮廓似然 轮廓的可靠构建 MLE 快速收敛序列 效应同质性下的推理 建模 未观察到的异质性 未观察到的协变量和边际剖面似然、梯度函数和 PNMLE 通过 EM 算法计算 PNMLE 剖面似然混合的 EMGFU 似然比检验和模型评估 中心分类 对心肌梗死后 β 受体阻滞剂效果的重新分析 建模 协变量信息 经典方法 剖面似然法 模型的应用 摘要 替代方法 近似似然模型 多级模型 比较剖面和近似似然 选择性管道净化的 MAIPD 分析 模拟研究比较的讨论 二项式轮廓似然 结合协变量信息和未观察到的异质性 观察到和未观察到的协变量的模型 应用 模型的简化 观察到和未观察到的协变量的模型 使用 CAMAP 开始使用 CAMAP 建模分析 结论 比值比估计 使用轮廓似然 效应同质性下的轮廓似然 建模协变量信息 MAIPD 中异质性的量化 问题作为二项式似然的轮廓似然 无条件方差及其估计 MAIPD 中的异质性测试 MAIPD 中异质性量的分析：案例研究 比较异质性方差的新估计和 DerSimonian-Laird 估计的模拟研究 欧洲的 Scrapie：作为 MAIPD 的多国监测研究 问题 无协变量的痒病监测数据 分析和结果 具有代表性协变量信息的数据 附录 导数二项式轮廓似然的计算 具有有界 Hesse 矩阵的目标函数的下界过程 轮廓似然比值比估计与 Mantel-Haenszel 估计器之间的联系 参考书目索引
Introduction The occurrence of meta-analytic studies with binary outcome Meta-analytic and multicenter studies Center or study effect Sparsity Some examples of MAIPDs Choice of effect measure The Basic Model Likelihood Estimation of relative risk in meta-analytic studies using the profile likelihood The profile likelihood under effect homogeneity Reliable construction of the profile MLE A fast converging sequence Inference under effect homogeneity Modeling Unobserved Heterogeneity Unobserved covariate and the marginal profile likelihood Concavity, the gradient function, and the PNMLE The PNMLE via the EM algorithm The EMGFU for the profile likelihood mixture Likelihood ratio testing and model evaluation Classification of centers A reanalysis on the effect of beta-blocker after myocardial infarction Modeling Covariate Information Classical methods Profile likelihood method Applications of the model Summary Alternative Approaches Approximate likelihood model Multilevel model Comparing profile and approximate likelihood Analysis for the MAIPD on selective tract decontamination Simulation study Discussion of this comparison Binomial profile likelihood Incorporating Covariate Information and Unobserved Heterogeneity The model for observed and unobserved covariates Application of the model Simplification of the model for observed and unobserved covariates Working with CAMAP Getting started with CAMAP Analysis of modeling Conclusion Estimation of Odds Ratio Using the Profile Likelihood Profile likelihood under effect homogeneity Modeling covariate information Quantification of Heterogeneity in an MAIPD The problem The profile likelihood as binomial likelihood The unconditional variance and its estimation Testing for heterogeneity in an MAIPD An analysis of the amount of heterogeneity in MAIPDs: a case study A simulation study comparing the new estimate and the DerSimonian-Laird estimate of heterogeneity variance Scrapie in Europe: A Multicountry Surveillance Study as an MAIPD The problem The data on scrapie surveillance without covariates Analysis and results The data with covariate information on representativeness Appendix Derivatives of the binomial profile likelihood The lower bound procedure for an objective function with a bounded Hesse matrix Connection between the profile likelihood odds ratio estimation and the Mantel-Haenszel estimator Bibliography Index