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As the roles of historical trials and real‐world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta‐analytic predictive prior (BaSe‐MAP) to incorporate time‐to‐event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data. [ABSTRACT FROM AUTHOR]

As the availability of real‐world data sources (eg, EHRs, claims data, registries) and historical data has rapidly surged in recent years, there is an increasing interest and need from investigators and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Bayesian meta‐analytic approaches are a popular historical borrowing method that has been developed to leverage such data using robust hierarchical models. The model structure accounts for various degrees of between‐trial heterogeneity, resulting in adaptively discounting the external information in the case of data conflict. In this article, we propose to integrate the propensity score method and Bayesian meta‐analytic‐predictive (MAP) prior to leverage external real‐world and historical data. The propensity score methodology is applied to select a subset of patients from external data that are similar to those in the current study with regards to key baseline covariates and to stratify the selected patients together with those in the current study into more homogeneous strata. The MAP prior approach is used to obtain stratum‐specific MAP prior and derive the overall propensity score integrated meta‐analytic predictive (PS‐MAP) prior. Additionally, we allow for tuning the prior effective sample size for the proposed PS‐MAP prior, which quantifies the amount of information borrowed from external data. We evaluate the performance of the proposed PS‐MAP prior by comparing it to the existing propensity score‐integrated power prior approach in a simulation study and illustrate its implementation with an example of a single‐arm phase II trial. [ABSTRACT FROM AUTHOR]

Background Recent evidence implicates inflammation as a key driver in delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage (SAH). Inducible nitric oxide synthase (iNOS) is one of the known major mediators of inflammation. We previously showed that an inhalational anesthetic, isoflurane, provides strong protection against delayed cerebral ischemia after SAH. Our current study aims to define the role of iNOS in isoflurane conditioning-induced protection against delayed cerebral ischemia in a mouse model of SAH. Methods and Results The experiments used 10- to 14-week-old male wild-type (C57BL/6) and iNOS global knockout mice. Anesthetic conditioning was initiated 1 hour after SAH with isoflurane 2% for 1 hour. Isoflurane-induced changes in iNOS expression were measured. N-(3-(aminomethyl) benzyl) acetamidine, a highly selective iNOS inhibitor, was injected intraperitoneally immediately after SAH and then daily. Vasospasm, microvessel thrombosis, and neurological assessment was performed. Data were analyzed by 1-way ANOVA and 2-way repeated measures ANOVA followed by Student Newman Keuls comparison test. Statistical significance was set at P <0.05. Isoflurane conditioning downregulated iNOS expression in naïve and SAH mice. N-(3-(aminomethyl) benzyl) acetamidine attenuated large artery vasospasm and microvessel thrombosis and improved neurological deficits in wild-type animals. iNOS knockout mice were significantly resistant to vasospasm, microvessel thrombosis, and neurological deficits induced by SAH. Combining isoflurane with N-(3-(aminomethyl) benzyl) acetamidine did not offer extra protection, nor did treating iNOS knockout mice with isoflurane. Conclusions Isoflurane conditioning-induced delayed cerebral ischemia protection appears to be mediated by downregulating iNOS. iNOS is a potential therapeutic target to improve outcomes after SAH.