The program should also provide methods for drug development programs with multiple endpoints. For now, this means that the program provides methods for two endpoints. Moreover, only normally distributed and time-to-event endpoints are implemented in the multiple endpoint setting. (Further extensions may be implemented in the future.) The definition of treatment success is different for the two endpoints:
In the time-to-event setting, the drug development program is defined to be successful if it proceeds from phase II to phase III and at least one endpoint shows a statistically significant treatment effect in phase III. For example, this situation is found in oncology trials, where overall survival (OS) and progression-free survival (PFS) are the two endpoints of interest.
For normally distributed endpoints, the drug development program is defined to be successful if it proceeds from phase II to phase III and all endpoints show a statistically significant treatment effect in phase III. For example, this situation is found in Alzheimer’s disease trials, where a drug should show significant results in improving cognition (cognitive endpoint) as well as in improving activities of daily living (functional endpoint).
The user should be able to provide the following input values in addition to the general parameters defined in the basic setting:
The program should correctly calculate the optimal sample size, the optimal threshold value and the corresponding expected utility for utility-based optimization of phase II/III programs with two time-to-event endpoints. We require the following:
As before, in addition to the main results of optimal sample size, optimal threshold value and expected utility, the program should be able to return the following additional data concerning the drug development program:
The effect size categories small, medium and large can be applied to both endpoints. In order to define an overall effect size from the two individual effect sizes, the package should implement combination rules. For normally distributed endpoints, two different combination rules should be implemented:
A strict rule assigning a large overall effect in case both endpoints show an effect of large size, a small overall effect in case that at least one of the endpoints shows a small effect, and a medium overall effect otherwise.
A relaxed rule assigning a large overall effect if at least one of the endpoints shows a large effect, a small effect if both endpoints show a small effect, and a medium overall effect otherwise.
Based on this we require the following features:
On the other hand, for time-to-event endpoints, the effect size of the endpoint with larger treatment effect should be selected as overall effect size. In addition, the user should be asked to provide two triples of benefits per category. If only the less important endpoint is significant after phase III, then the smaller benefit triple should be chosen by the software. If at least the more important endpoint is significant, then the larger benefit triple should be chosen by the software. (This combination rule reflects the situation in oncological trials: If only progression-free survival is significant, then a smaller benefit can be expected compared to trials were over all survival is significant.) Based on this we require the following features:
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