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Meta‐analyses are commonly performed based on random‐effects models, while in certain cases one might also argue in favor of a common‐effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta‐analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phase III of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often be misleading. [ABSTRACT FROM AUTHOR]

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; [ABSTRACT FROM AUTHOR]

The analysis of count data is commonly done using Poisson models. Negative binomial models are a straightforward and readily motivated generalization for the case of overdispersed data, that is, when the observed variance is greater than expected under a Poissonian model. Rate and overdispersion parameters then need to be considered jointly, which in general is not trivial. Here, we are concerned with evidence synthesis in the case where the reporting of data is rather heterogeneous, that is, events are reported either in terms of mean event counts, the proportion of event-free patients, or rate estimates and standard errors. Either figure carries some information about the relevant parameters, and it is the joint modeling that allows for coherent inference on the parameters of interest. The methods are motivated and illustrated by a systematic review in chronic obstructive pulmonary disease. [ABSTRACT FROM AUTHOR]