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المؤلفJu, Ke
المؤلفLin, Lifeng
المؤلفChu, Haitao
المؤلفCheng, Liang Liang
المؤلفXu, Chang
تاريخ الإتاحة2021-02-08T06:15:10Z
تاريخ النشر2020-06-11
اسم المنشورBMC Medical Research Methodology
المعرّفhttp://dx.doi.org/10.1186/s12874-020-01035-6
الاقتباسJu, K., Lin, L., Chu, H. et al. Laplace approximation, penalized quasi-likelihood, and adaptive Gauss–Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data. BMC Med Res Methodol 20, 152 (2020). https://doi.org/10.1186/s12874-020-01035-6
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086620524&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/17567
الملخص© 2020 The Author(s). Background: In meta-analyses of a binary outcome, double zero events in some studies cause a critical methodology problem. The generalized linear mixed model (GLMM) has been proposed as a valid statistical tool for pooling such data. Three parameter estimation methods, including the Laplace approximation (LA), penalized quasi-likelihood (PQL) and adaptive Gauss-Hermite quadrature (AGHQ) were frequently used in the GLMM. However, the performance of GLMM via these estimation methods is unclear in meta-analysis with zero events. Methods: A simulation study was conducted to compare the performance. We fitted five random-effects GLMMs and estimated the results through the LA, PQL and AGHQ methods, respectively. Each scenario conducted 20,000 simulation iterations. The data from Cochrane Database of Systematic Reviews were collected to form the simulation settings. The estimation methods were compared in terms of the convergence rate, bias, mean square error, and coverage probability. Results: Our results suggested that when the total events were insufficient in either of the arms, the GLMMs did not show good point estimation to pool studies of rare events. The AGHQ method did not show better properties than the LA estimation in terms of convergence rate, bias, coverage, and possibility to produce very large odds ratios. In addition, although the PQL had some advantages, it was not the preferred option due to its low convergence rate in some situations, and the suboptimal point and variance estimation compared to the LA. Conclusion: The GLMM is an alternative for meta-analysis of rare events and is especially useful in the presence of zero-events studies, while at least 10 total events in both arms is recommended when employing GLMM for meta-analysis. The penalized quasi-likelihood and adaptive Gauss-Hermite quadrature are not superior to the Laplace approximation for rare events and thus they are not recommended.
اللغةen
الناشرBMC
الموضوعBoth-arm zero events
Meta-analysis
One-stage approach
Rare events
العنوانLaplace approximation, penalized quasi-likelihood, and adaptive Gauss-Hermite quadrature for generalized linear mixed models: Towards meta-analysis of binary outcome with sparse data
النوعArticle
رقم العدد1
رقم المجلد20
ESSN1471-2288
dc.accessType Open Access


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