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Bayesian Adaptive Methods for Clinical Trials (ISBN-13: 978-1439825488), by S.M. Berry, B.P. Carlin, J.J. Lee, and P. Muller, Boca Raton, FL: Chapman and Hall/CRC Press, 2011.
Here are electronic versions of most of the data sets, R code, WinBUGS code, or freely downloadable sofatware packages and their chapter and page number(s) in the book -- please help yourself!

Chapter 2
​Page 22:
nndon.r
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Page 24: 
nndon2.r
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Page 35: 
beta194.r
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Page 38:
betapriors.r
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Page 39:
betaposts.r
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Page 53:
janicak.txt
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Page 54:
beta10745.r
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Page 55:
beta4by4plot.r
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Pages 58-59:
betabinhm_bugs.txt
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Pages 58-59:
betabinhm_data.txt
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Pages 58-59:
betabinhm_inits.txt
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Pages 59-62:
betabinhm.r
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Pages 59-62:
betabinhm_new.r
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Page 75:
refmodel.txt
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Chapter 3
Page 100:
(zip file with Jack's program phaseIsim.R)
phaseisim.zip
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Page 100:
(Brad's version of Jack's program; bottom of file has function calls that create Table 3.2, Scenario 1.)
phaseisim_r.txt
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Page 101:
crmdemo.zip
File Size: 7 kb
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Page 101:
crmexplore.zip
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Page 101:
crminteractive.zip
File Size: 16 kb
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Pages 104-105:
ewoc.txt
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Page 106:
titecrm.r
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Page 110: 
(R code for method for toxicity intervals)
324.r
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Page 132:
(R code for the 2-agent dose escalation method)
354.r
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Chapter 4
Page 149:
(standalone desktop program to implement the Phase II predictive probability designs of the sort illustrated in Example 4.2)
Phase II PP Design
Page 152:
(the "lean" version of the Multc clinical trial software that enables stopping for futility, efficacy, or toxicity with binary data
MultcLean
Page 152:
(simple R implementation of this same basic approach, e.g., Thall Simon and Estey, 1995)
431.r
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Page 156:
(MD Anderson software package used to implement Algorithm 4.3 in Example 4.4)
Adaptive Randomization (AR)
Pages 164-157:
(basic R implementation of the optimal biologic dosing method (Algorithm 4.4) using the FFBS method (Algorithm 4.5) in Example 4.5
35.r
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Page 171:
(basic R implementation of the Huang et al (2009) method adaptive allocation with survival response, illustrated in Example 4.7)
443.r
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Page 171:
(helpful debugging note for previous code from Dr. John Reynolds of Monash University)
443.txt
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Page 180:
(package written by David Rossell for implementing the decision-theoretic screening design of Algorithm 4.9; see bottom of page)
seqdesphII
Chapter 5
Page 197:
(R program to compute the exact Type I error of the basic Phase III confirmatory trial, Subsection 5.2.1)
521.r
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Pages 197-198:
(R program to simulate the Type I error and other operating characteristics of the basic Phase III confirmatory trial, Example 5.2)
adapt.r
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Pages 205-207:
(R program to simulate operating characteristics of the basic confirmatory trial with delayed outcomes, Example 5.4)
example5.4.r
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Pages 214-215:
(R program for calculating the predictive probabilities at the 50- or 75-subject interim analyses for the confirmatory trial with auxiliary variables, Example 5.6)
example5.6.r
File Size: 1 kb
File Type: r
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Chapter 6
Page 251:
(R [BRugs] program to simulate the power curve for the standard hierarchical model for incorporating historical data, in the case of Example 6.1, testing for a treatment effect in the current trial)
nn_beta1_r.txt
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Page 251:
(WinBUGS code called by the preceding BRugs program)
nn_bugs.txt
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Pages 251-252:
(R [BRugs] program to simulate the power curve for the standard hierarchical model for incorporating historical data, in the case of Example 6.2, testing whether ``to pool or not to pool'')
nn_delta_r.txt
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Pages 251-252:
(WinBUGS code called by the preceding BRugs program)
nn_bugsdelta.txt
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Pages 257-260:
(online software for the design and analysis of clinical trials that incorporate historical controls via commensurate priors, for Gaussian data [Example 6.3] and also survival data assuming a piecewise constant hazard)
SMEEACT online package
Page 267:
(WinBUGS program to implement the bioequivalence model of Ghosh and Gonen (2008), Subsection 6.2.3)                                           
mv_abe_winbugs.txt
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File Type: txt
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Finally, to show we're not absolutists...
Standard two-sample frequentist clinical trial design tool:
Online two-sample t-test sample size calculator
Copyright 2020 Counterpoint Statistical Consulting, LLC

  • Home
  • Who We Are
  • What We Offer
  • Books and Software
    • Bayesian Methods for Data Analysis
    • Hierarchical Modeling and Analysis for Spatial Data
    • Bayesian Adaptive Methods for Clinical Trials
    • Software and peer-reviewed literature
  • Contact Us