Statistics and Data Science Seminar
Li Wei
UIC
Stochastic Curtailment under Linear Models
Abstract: Stochastic curtailment, 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 derived 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 is
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 here fails to have this
characteristic in general. A necessary and sufficient condition for the
conditional power to depend soly on the interested contrast is provided 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.
Wednesday November 29, 2006 at 3:30 PM in SEO 512