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gsbDesign: An R Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design

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  • Gerber, Florian
  • Gsponer, Thomas

Abstract

The R package gsbDesign provides functions to evaluate the operating characteristics of Bayesian group sequential clinical trial designs. More specifically, we consider clinical trials with interim analyses, which compare a treatment with a control, and where the endpoint is normally distributed. Prior information can either be specified for the difference of treatment and control, or separately for the effects in the treatment and the control groups. At each interim analysis, the decision to stop or continue the trial is based on the posterior distribution of the difference between treatment and control. The decision at the final analysis is also based on this posterior distribution. Multiple success and/or futility criteria can be specified to reflect adequately medical decision-making. We describe methods to evaluate the operating characteristics of such designs for scenarios corresponding to different true treatment and control effects. The characteristics of main interest are the probabilities of success and futility at each interim analysis, and the expected sample size. We illustrate the use of gsbDesign with a detailed case study.

Suggested Citation

  • Gerber, Florian & Gsponer, Thomas, 2016. "gsbDesign: An R Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i11).
    • Handle: RePEc:jss:jstsof:v:069:i11
    DOI: http://hdl.handle.net/10.18637/jss.v069.i11
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    References listed on IDEAS

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    1. Bart E. Burington & Scott S. Emerson, 2003. "Flexible Implementations of Group Sequential Stopping Rules Using Constrained Boundaries," Biometrics, The International Biometric Society, vol. 59(4), pages 770-777, December.
    2. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
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