scholarly journals Empirical comparison of univariate and multivariate meta‐analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

2019 ◽  
Vol 10 (3) ◽  
pp. 440-451
Author(s):  
Malcolm J. Price ◽  
Helen A. Blake ◽  
Sara Kenyon ◽  
Ian R. White ◽  
Dan Jackson ◽  
...  
Keyword(s):  
Binary Outcomes ◽  
Empirical Comparison ◽  
Pregnancy And Childbirth ◽  
Meta Analyses

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

scholarly journals The impact of covariance priors on arm‐based Bayesian network meta‐analyses with binary outcomes

2020 ◽  
Vol 39 (22) ◽  
pp. 2883-2900 ◽  
Author(s):  
Zhenxun Wang ◽  
Lifeng Lin ◽  
James S. Hodges ◽  
Haitao Chu
Keyword(s):  
Bayesian Network ◽  
Binary Outcomes ◽  
Meta Analyses ◽  
The Impact

scholarly journals An empirical comparison of meta-analyses of published gene-disease associations versus consortium analyses

2009 ◽  
Vol 11 (3) ◽  
pp. 153-162 ◽  
Author(s):  
A Cecile J W Janssens ◽  
Angela M González-Zuloeta Ladd ◽  
Sandra López-Léon ◽  
John P A Ioannidis ◽  
Ben A Oostra ◽  
...  
Keyword(s):  
Empirical Comparison ◽  
Disease Associations ◽  
Meta Analyses

An empirical comparison of heterogeneity variance estimators in 12 894 meta-analyses

2015 ◽  
Vol 6 (2) ◽  
pp. 195-205 ◽  
Author(s):  
Dean Langan ◽  
Julian P. T. Higgins ◽  
Mark Simmonds
Keyword(s):  
Empirical Comparison ◽  
Variance Estimators ◽  
Heterogeneity Variance ◽  
Meta Analyses

scholarly journals Aspirin to Prevent Preeclampsia

2020 ◽  
Vol 32 (2) ◽  
pp. 106-116
Author(s):  
Ferdousi Begum ◽  
TA Chowdhury
Keyword(s):  
Preterm Birth ◽  
High Risk ◽  
Low Dose ◽  
Growth Restriction ◽  
Mortality And Morbidity ◽  
Pregnancy And Childbirth ◽  
Dose Aspirin ◽  
Low Dose Aspirin ◽  
Adverse Pregnancy ◽  
Meta Analyses

Background: Pre-eclampsia is a major cause of mortality and morbidity during pregnancy and childbirth. There are recommendations on use of medications to prevent preeclampsia, including low dose aspirin. Objective: The objective of this review is to discuss role of aspirin in reducing the incidence and maternal mortality and morbidity due to preeclampsia including its dose and duration of use. Methods: Review of available literature in internet and from libraries. Results: Four large randomized trials have demonstrated a reduction in the incidence of preeclampsia in patients treated with low-dose aspirin prophylaxis compared with placebo/ no treatment (15 versus 19 percent, 18 versus 20 percent, 6.7 versus 7.6 percent, and 1.6 versus 4.3 percent); however, the results were statistically significant in only the last trial. When data from these and other trials were pooled, meta-analyses supported the significance of the trend observed in individual trials. When begun early in the second trimester, use of low-dose aspirin (75-150 mg) reduced the incidence of preeclampsia by at least 10 percent, with the greatest absolute benefit in women at moderate to high risk of developing the disease. Serious sequelae of early onset preeclampsia, such as preterm birth and fetal growth restriction, were also reduced. Conclusion: Low-dose aspirin reduces the frequency of preeclampsia, as well as related adverse pregnancy outcomes (preterm birth, growth restriction), by about 10 to 20 percent when given to women at moderate to high risk of the disease. It has an excellent maternal/ fetal safety profile in pregnancy. WHO recommends Low-dose acetylsalicylic acid (aspirin, 75 mg) for the prevention of preeclampsia in women at high risk of developing the condition; should be initiated before 20 weeks of pregnancy. It should be taken preferably from 13th week of pregnancy, daily, regularly and may be discontinued 5 to 10 days before delivery. Bangladesh J Obstet Gynaecol, 2017; Vol. 32(2) : 106-116

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