Synthesis of evidence from zero‐events studies: A comparison of one‐stage framework methods

Abstract

In evidence synthesis, dealing with zero‐events studies is an important and complicated task that has generated broad discussion. Numerous methods provide valid solutions to synthesizing data from studies with zero‐events, either based on a frequentist or a Bayesian framework. Among frequentist frameworks, the one‐stage methods have their unique advantages to deal with zero‐events studies, especially for double‐arm‐zero‐events. In this article, we give a concise overview of the one‐stage frequentist methods. We conducted simulation studies to compare the statistical properties of these methods to the two‐stage frequentist method (continuity correction) for meta‐analysis with zero‐events studies when double‐zero‐events studies were included. Our simulation studies demonstrated that the generalized estimating equation with unstructured correlation and beta‐binomial method had the best performance among the one‐stage methods. The random intercepts generalized linear mixed model showed good performance in the absence of obvious between‐study variance. Our results also showed that the continuity correction with inverse‐variance heterogeneous (IVhet) analytic model based on the two‐stage framework had good performance when the between‐study variance was obvious and the group size was balanced for included studies. In summary, the one‐stage framework has unique advantages to deal with studies with zero events and is not susceptive to group size ratio. It should be considered in future meta‐analyses whenever possible.

Random‐effects meta‐analysis models for the odds ratio in the case of rare events under different data‐generating models: A simulation study

“…A further simulation study by Mathes and Kuss (2018), which examined the performance of the beta-binomial model for meta-analyses of small numbers of studies, confirmed its good performance in random-effects meta-analysis in comparison with the inverse variance model. Xu et al (2021) confirmed the suitability of the beta-binomial model for rare events meta-analysis in a recent simulation study, but the authors claim that at least 10 events in each arm are required to obtain a good performance.…”

“…Different variants of GEE models were subject of the simulation study by Xu et al. (2021), and based on their results, further investigations of such models for a larger variety of simulation conditions seem worthwhile. Bayesian models have shown promising results for meta‐analyses of small numbers of studies with small sample sizes when weakly informative priors were used (Friede et al., 2017; Günhan et al., 2019; Pateras et al., 2020).…”

“…It is unclear whether it is reasonable to evaluate the performance of Betabin under the DGMs pCFixed and pRandom in the first place, because both are binomial‐normal models, and thus, use a different random‐effects distribution than Betabin. Nevertheless, both DGMs have been used in past as well as in recent simulation studies evaluating Betabin (for pCFixed , see Kuss, 2014; Mathes & Kuss, 2018 for pRandom , see Xu et al., 2021). In both DGMs, the between‐study model is parameterized in terms of the distribution of $$\mbox{logit}(\pi _{i0})$$ and $$\mbox{logit}(\pi _{i1})$$, as outlined above.…”

“…Xu et al. (2021) confirmed the suitability of the beta‐binomial model for rare events meta‐analysis in a recent simulation study, but the authors claim that at least 10 events in each arm are required to obtain a good performance.…”

Validity of data extraction in evidence synthesis practice of adverse events: reproducibility study

“…We used Stata 15/SE for the data analysis. The estimation of the proportions was based on the meglm command under the Poisson function with the log link 28 ; we set α=0.05 as the significance level. We performed the re-evaluation of the meta-analyses by the admetan command in Stata and verified by metafor command in R 3.5.1 software, and Excel 2013 was used for visualisation.…”