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Background Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial’s analysis through a prior distribution. Methods Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions. Results The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data. Conclusion In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings. Electronic supplementary material The online version of this article (10.1186/s12874-019-0714-z) contains supplementary material, which is available to authorized users.

International audience; Please cite this article in press as: Tellez-Gabriel, M., et al., Analysis of gap junctional intercellular communications using a dielectrophoresis-based microchip. Gap junctions are transmembrane structures that directly connect the cytoplasm of adjacent cells, making intercellular communications possible. It has been shown that the behaviour of several tumours – such as bone tumours – is related to gap junction intercellular communications (GJIC). Several methodologies are available for studying GJIC, based on measuring different parameters that are useful for multiple applications, such as the study of carcinogenesis for example. These methods nevertheless have several limitations. The present manuscript describes the setting up of a dielectrophoresis (DEP)-based lab-on-a-chip platform for the real-time study of Gap Junctional Intercellular Communication between osteosarcoma cells and the main cells accessible to their microenvironment. We conclude that using the DEParray technology for the GJIC assessment has several advantages comparing to current techniques. This methodology is less harmful for cells integrity; cells can be recovered after interaction to make further molecular analysis; it is possible to study GJIC in real time; we can promote cell interactions using up to five different populations. The setting up of this new methodology overcomes several difficulties to perform experiments for solving questions about GJIC process that we are not able to do with current technics.