Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls

Nan Chen, Bradley P. Carlin, Brian P. Hobbs

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


A collection of web-based statistical tools (http://research.mdacc.tmc.edu/SmeeactWeb/) are described that enable investigators to incorporate historical control data into analysis of randomized clinical trials using Bayesian hierarchical modeling as well as implement adaptive designs that balance posterior effective sample sizes among the study arms and thus maximize power. With balanced allocation guided by “dynamic” Bayesian hierarchical modeling, the design offers the potential to assign more patients to experimental therapies and thereby enhance efficiency while limiting bias and controlling average type I error. The tools effectuate analysis and design for static (non-hierarchical Bayesian analysis) and two types of dynamic (hierarchical Bayesian inference using empirical Bayes and spike-and-slab hyperprior) methods for Gaussian data models, as well as a dynamic method for time-to-failure endpoints based on a piecewise constant hazard model. The site also offers interfaces to facilitate calibration of the model hyperparameters. These allow users to test different parameters in the presence of the historical data on the basis of their resultant frequentist properties, including bias and mean squared error. All calculations are performed on a central computational server. The user may upload data, choose trial settings, run computations in real-time, and review the results using only a web browser. The back-end web module, computation module, and MCMC sampling module are developed in the C#, R, and C++ languages, respectively, and a communication module is also available to ensure the continued connection between the client computer and the back-end server during the Bayesian computations. The statistical tools are described and demonstrated with examples.

Original languageEnglish (US)
Pages (from-to)50-68
Number of pages19
JournalComputational Statistics and Data Analysis
StatePublished - Nov 2018

Bibliographical note

Funding Information:
We thank Clift Norris for his help on the web-site design, Sylvain Laroche for the server management. The work was supported in part by National Cancer Institute grants R01-CA157458 and P30-CA016672 .

Publisher Copyright:
© 2018 Elsevier B.V.


  • Adaptive design
  • Bayesian analysis
  • Clinical trials
  • Hierarchical modeling
  • Historical controls
  • MCMC
  • Simulation


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