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Open Access Highly Accessed Research

Predicting clinical trial results based on announcements of interim analyses

Kristine R Broglio1, David N Stivers2 and Donald A Berry13*

Author Affiliations

1 Berry Consultants, LLC, 4301 Westbank Dr, Suite 140, Bldg B, Austin, TX 78746, USA

2 Alere, 9975 Summers Ridge Rd, San Diego, CA 92121, USA

3 University of Texas M.D. Anderson Cancer Center, P.O. Box 310402, Houston, TX 77230, USA

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Trials 2014, 15:73  doi:10.1186/1745-6215-15-73

Published: 7 March 2014

Abstract

Background

Announcements of interim analyses of a clinical trial convey information about the results beyond the trial’s Data Safety Monitoring Board (DSMB). The amount of information conveyed may be minimal, but the fact that none of the trial’s stopping boundaries has been crossed implies that the experimental therapy is neither extremely effective nor hopeless. Predicting success of the ongoing trial is of interest to the trial’s sponsor, the medical community, pharmaceutical companies, and investors. We determine the probability of trial success by quantifying only the publicly available information from interim analyses of an ongoing trial. We illustrate our method in the context of the National Surgical Adjuvant Breast and Bowel (NSABP) trial, C-08.

Methods

We simulated trials based on the specifics of the NSABP C-08 protocol that were publicly available. We quantified the uncertainty around the treatment effect using prior weights for the various possibilities in light of other colon cancer studies and other studies of the investigational agent, bevacizumab. We considered alternative prior distributions.

Results

Subsequent to the trial’s third interim analysis, our predictive probabilities were: that the trial would eventually be successful, 48.0%; would stop for futility, 7.4%; and would continue to completion without statistical significance, 44.5%. The actual trial continued to completion without statistical significance.

Conclusions

Announcements of interim analyses provide information outside the DSMB’s sphere of confidentiality. This information is potentially helpful to clinical trial prognosticators. ‘Information leakage’ from standard interim analyses such as in NSABP C-08 is conventionally viewed as acceptable even though it may be quite revealing. Whether leakage from more aggressive types of adaptations is acceptable should be assessed at the design stage.

Keywords:
Predictive probabilities; DSMB; Interim analyses; Operational bias; Prior distribution; Bevacizumab; Colon cancer