A predictive probability design for phase II cancer clinical trials

Abstract

Background-Two-or three-stage designs are commonly used in phase II cancer clinical trials. These designs possess good frequentist properties and allow early termination of the trial when the interim data indicate that the experimental regimen is inefficacious. The rigid study design, however, can be difficult to follow exactly because the response has to be evaluated at prespecified fixed number of patients.Purpose-Our goal is to develop an efficient and flexible design that possesses desirable statistical properties.Method-A flexible design based on Bayesian predictive probability and the minimax criterion is constructed. A three-dimensional search algorithm is implemented to determine the design parameters.Results-The new design controls type I and type II error rates, and allows continuous monitoring of the trial outcome. Consequently, under the null hypothesis when the experimental treatment is not efficacious, the design is more efficient in stopping the trial earlier, which results in a smaller expected sample size. Exact computation and simulation studies demonstrate that the predictive probability design possesses good operating characteristics.Limitations-The predictive probability design is more computationally intensive than two-or three-stage designs. Similar to all designs with early stopping due to futility, the resulting estimate of treatment efficacy may be biased.Conclusions-The predictive probability design is efficient and remains robust in controlling type I and type II error rates when the trial conduct deviates from the original design. It is more adaptable than traditional multi-stage designs in evaluating the study outcome, hence, it is easier to implement. S-PLUS/R programs are provided to assist the study design.

Adaptive Clinical Trials

“…More details on sequential designs can be found in Ghosh and Sen (14), Jennison and Turnbull (15), and Proschan, Lan and Wittes (16). More information on Bayesian sequential stopping rules can be found in Thall et al (42, 43), Lee and Liu (44), and Berry et. al (21).…”

Real-time prediction of clinical trial enrollment and event counts: A review

“…DSMBs may wish to apply the predictive power in an interim efficacy or futility analysis, in addition to or in lieu of the conditional power. Lee and Liu [29] have proposed using the predictive power as an early stopping criterion in phase II cancer trials.…”

Controlled multi-arm platform design using predictive probability

“…The decision to stop the trial (on the basis of absence of effect or strong evidence for improvement) or continue enrolling patients (because of a lack of convincing evidence to inform a decision) is reassessed throughout the conduct of the trial until a maximum sample size has been attained. Lee and Liu [20] introduced another Bayesian method for continuous monitoring of a single-arm phase II trial based on the predictive probability (PP) of a successful result at the end of the trial given continuation to a pre-specified sample size.…”

“…PP-derived rules can be applied uniformly at any time during the trial without the need to specify and calibrate an arbitrary burn-in period or impose ad-hoc rules for adjusting the decision thresholds in relation to interim sample size (such as those proposed in [35]). Moreover, they have been shown to yield rejection regions with smoother transitions when compared with posterior methods, and provide higher early stopping probability under null scenarios [20, 25]. The following sections demonstrate how PP-derived futility monitoring can be used to design and implement a continuous screening platform that is calibrated to deliver desirable frequentist properties given a pre-specified maximum number of comparisons.…”