Meta‐analysis of clinical trials with rare events

DOI10.1002/bimj.201400184zbMath1329.62032OpenAlexW2109489289WikidataQ50924951 ScholiaQ50924951MaRDI QIDQ3451375

Kalliopi Mylona, Alan C. Kimber, Dankmar Boehning

Publication date: 16 November 2015

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201400184

zbMATH Keywords

meta-analysisrare event analysisMantel-Haenszel estimationPoisson regression with random effectszero and double-zero studies

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (11)

On the exact null-distribution of a test for homogeneity of the risk ratio in meta-analysis of studies with rare events ⋮ Meta-Analysis for Rare Events As Binary Outcomes ⋮ Multivariate meta‐analysis with an increasing number of parameters ⋮ Frequentist performances of Bayesian prediction intervals for random‐effects meta‐analysis ⋮ Random‐effects meta‐analysis models for the odds ratio in the case of rare events under different data‐generating models: A simulation study ⋮ When should meta‐analysis avoid making hidden normality assumptions? ⋮ Nonparametric estimation of effect heterogeneity in rare events meta-analysis: bivariate, discrete mixture model ⋮ Bivariate beta-binomial model using Gaussian copula for bivariate meta-analysis of two binary outcomes with low incidence ⋮ Performance of the Peto odds ratio compared to the usual odds ratio estimator in the case of rare events ⋮ Investigating heterogeneity in meta-analysis of studies with rare events. Estimating the amount of heterogeneity ⋮ Estimation of effect heterogeneity in rare events meta-analysis

Uses Software

• SAS
• SAS/STAT
• COUNT

Cites Work

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• Negative Binomial Regression
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• Meta-analysis of Binary Data Using Profile Likelihood
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
• Estimating Risk Difference in Multicenter Studies Under Baseline‐Risk Heterogeneity
• Meta-Analysis of Rare Binary Adverse Event Data

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