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

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

Abstract

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.

Suggested Citation

  • Chen, Nan & Carlin, Bradley P. & Hobbs, Brian P., 2018. "Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 50-68.
    • Handle: RePEc:eee:csdana:v:127:y:2018:i:c:p:50-68
    DOI: 10.1016/j.csda.2018.05.002
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    References listed on IDEAS

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    1. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    2. De Santis, Fulvio, 2006. "Power Priors and Their Use in Clinical Trials," The American Statistician, American Statistical Association, vol. 60, pages 122-129, May.
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    4. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    5. Thomas A. Murray & Brian P. Hobbs & Theodore C. Lystig & Bradley P. Carlin, 2014. "Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data," Biometrics, The International Biometric Society, vol. 70(1), pages 185-191, March.
    6. Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
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