RESUMEN
In group sequential trials, the interim results are more promising in the early termination of a trial either for efficacy or futility of the trial. This reduces the cost and time implications. Moreover, interim analyses play a key role to tackle the problem that arises due to adverse effects. In concern with the early stopping of trials, there are numerous stopping methods among them the Conditional power (CP) approach is best recommended. The CP approach provides the probability of getting significant results at the end of the study given the data observed so far. There are very few Indian studies that had incorporated the concept of CP and made decisions based on the results. This study popularises the CP approach detailing computing and its interpretation and is thereby facilitating clinical researchers to use this approach effectively. We have used real-time and hypothetical examples and illustrated the concept of CP under trend, CP under null, and CP under design. We suggest stopping the study for efficacy when the CP under null is greater than 80% and stopping the study for futility when CP under trend is less than 30%.
RESUMEN
The outbreak of coronavirus disease 2019 (COVID-19) led to the first-level response to major public health emergencies in China. To explore the therapy of COVID-19, hundreds of clinical studies were conducted. For major public health emergencies, it is important to find out the effective drug and therapeutic regimen as soon as possible for the control of disease, which raises the claim of timeliness to the trials conducted in emergency, especially to the registered clinical trials. This paper discusses the choice of clinical endpoint and related questions in the design of clinical trials in emergency from the statistical perspective.
RESUMEN
Adaptive design is a trial design that allows modifications to some aspects of the trial after its initiation without undermining the validity and integrity of the trial. Adaptive design makes it possible to discover and rectify inappropriate assumptions in trial designs, reduce development costs and the time to market. It has been very attractive to the pharmaceutical industries. In this paper, it is not focused on adaptive designs for clinical trials with multiple endpoints studied using computer simulations, but is to clarify our evolution, which is taken into consideration in a trial adaptive design at present, if need modifications.