Stochastic curtailment method under linear models.

摘要

Stochastic curtailment method, one of the major statistical tools adopted in interim analysis, has attracted more attentions than its competitors such as group sequential procedures for it integrates current data and potential future outcomes in addition to its simplicity in design and implementation. Under this approach, the conditional power, which is the probability of rejecting the null hypothesis at the planned end of the study given the accumulating data, is calculated and the stopping decision is made according to the comparison of this power with a pre-specified threshold. Many procedures with this perspective have been developed for interim analysis. However, possibly for the purpose of statistical convenience, only trials with one or two arms are investigated. Here, we derive an analytic formula for the conditional power under the frame of linear models so that it can be applied to most actual clinical trials in which multiple treatment effects, block effects and covariate effects are all allowed to be considered. The properties of this conditional power are investigated and further our research shows that, unlike the standard power of a regular test for a treatment contrast which depends on unknown parameters only through the contrast itself, the conditional power fails to have this characteristic in general. A necessary and sufficient condition for the conditional power to depend solely on the interested contrast is established and some instances are illustrated. Similar arguments can be made about the sufficient statistics for the conditional power. Finally, the results obtained here is applied to an interim analysis performed in a multi-center, randomized, double-blinded, placebo-controlled, parallel group phase II study where centers act as blocks and baseline scores are treated as covariates, resulting in an early termination of the trial and hence a substantial saving in cost.