Curtailed phase II binary outcome trials and adaptive multi-outcome trials

Phase II clinical trials are a critical aspect of the drug development process. With drug development costs ever increasing, novel designs that can improve the efficiency of phase II trials are extremely valuable. Phase II clinical trials for cancer treatments often measure a binary outcome. The final trial decision is generally to continue or cease development. When this decision is based solely on the result of a hypothesis test, the result may be known with certainty before the planned end of the trial. Unfortunately though, there is often no opportunity for early stopping when this occurs. Some existing designs do permit early stopping in this case, accordingly reducing the required sample size and potentially speeding up drug development. However, more improvements can be achieved by stopping early when the final trial decision is very likely, rather than certain, known as stochastic curtailment. While some authors have proposed approaches of this form, these approaches have limitations, such as relying on simulation, considering relatively few possible designs and not permitting early stopping when a treatment is promising. In this thesis we address these limitations by proposing design approaches for single-arm and two-arm phase II binary outcome trials. We use exact distributions, avoiding simulation, consider a wider range of possible designs and permit early stopping for promising treatments. As a result, we are able to obtain trial designs that have considerably reduced sample sizes on average. Following this, we switch attention to consider the fact that clinical trials often measure multiple outcomes of interest. Existing multi-outcome designs focus almost entirely on evaluating whether all outcomes show evidence of efficacy or whether at least one outcome shows evidence of efficacy. While a small number of authors have provided multi-outcome designs that evaluate when a general number of outcomes show promise, these designs have been single-stage in nature only. We therefore propose two designs, of group-sequential and drop the loser form, that provide this design characteristic in a multi-stage setting. Previous such multi-outcome multi-stage designs have allowed only for a maximum of two outcomes; our designs thus also extend previous related proposals by permitting any number of outcomes