This is astonishing as unfavorable futility boundaries may have serious consequences with respect to the performance of the study design. The choice of adequate boundaries to stop the study early for futility has been discussed only briefly in these works. The second approach is to define futility stopping boundaries either in isolation or in conjunction with group sequential efficacy boundaries. This conditional approach can further be divided into stochastic curtailment, a frequentist approach, and methods based on the predictive power or the predictive probability, which are partially or fully Bayesian methods. The first approach is a conditional one, where the study is stopped for futility, if the conditional power falls under a prespecified threshold. With respect to futility stopping, there are mainly two fundamental approaches in the statistical literature. A comprehensive overview and general concepts of the statistical methodology in group sequential designs are provided by Jennison and Turnbull. Since the pioneering works of Pocock and O’Brien and Fleming, these designs have been implemented since long in clinical trial routine. Decision rules to stop a trial early for efficacy have been broadly investigated. Generally, the study can either be stopped for efficacy if the study goal is prematurely achieved or for futility, if reaching the aim of the trial seems desperate. Group sequential designs give the opportunity to stop the study early during an interim analysis, thereby saving time and financial resources. In recent years, the flexibility and efficiency of clinical trials became increasingly important, in particular for trials from the pharmaceutical industry. ConclusionsĪs the properties of futility boundaries are often not considered in practice and unfavorably chosen futility boundaries may imply bad properties of the study design, we recommend assessing the performance of these boundaries according to the criteria proposed in here. Our results clearly demonstrate the benefit of using such ‘optimal’ futility boundaries, especially compared to futility boundaries commonly applied in practice. Resultsīy construction, the provided method of choosing futility boundaries maximizes the probability to correctly stop in case of small or opposite effects while limiting the power loss and the probability of stopping the study ‘wrongly’. Our methods are illustrated by a real clinical trial example and by Monte-Carlo simulations. Further, we present three different group sequential designs for two endpoints applying these futility boundaries. In this work, we propose a general method to construct ‘optimal’ futility boundaries according to predefined criteria. Whereas the choice of adequate boundaries to stop a trial early for efficacy has been broadly discussed in the literature, the choice of optimal futility boundaries has not been investigated so far, although this may have serious consequences with respect to performance characteristics. ![]() For this reason, so-called ‘group sequential designs’ are of particular importance in this setting. Assessing the feasibility of such a trial is therefore difficult, as the number of parameter assumptions to be correctly specified is large. For example, when including two primary endpoints in the confirmatory analysis, the power of the trial depends on the effects of both endpoints and on their correlation. This is especially important, if the planning assumptions required for power calculation are based on a low level of evidence. Network_pile(optimizer=RMSprop(lr=lr), loss=LossFunc, loss_weights=lossWeights, metrics=)Īction, value = np.reshape(, (1, -1)), np.In clinical trials, the opportunity for an early stop during an interim analysis (either for efficacy or for futility) may relevantly save time and financial resources. # STEP-4: complie using LossFunc and lossWeights dicts Network_model = Model(inputs=, outputs = ) I am trying to write a custom loss function My network has two outputs and single input.
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