| Title | BayesProp: Bayesian Clinical Trial Design for Regression Models Using Historical Data (SAS) |
| Publication Type | Software |
| Year of Publication | 2017 |
| Authors | Ibrahim, Joseph G., and Matthew Psioda |
| Abstract | PP1.SAS Estimates the Bayesian type I error rate and power using truncated sampling priors (K=2) and based on the partial-borrowing power prior, the supervised power prior, and the modified critical value approach. In the body of the program, the user can specify design parameters that are used in the simulation. NORMALIZED_PP1.SAS Estimates the Bayesian type I error rate and power using truncated sampling priors (K=2) and based on the normalized power prior. In the body of the program, the user can specify design parameters that are used in the simulation MAP_PRIOR1.SAS Estimates the Bayesian type I error rate and power using truncated sampling priors (K=2) and based on the two-component robust mixture prior. In the body of the program, the user can specify design parameters that are used in the simulation. In particular, the following %LET statements define key macro variables AMAP_PRIOR1.SAS Estimates the Bayesian type I error rate and power using truncated sampling priors (K=2) and based on the two-component robust mixture prior in an adaptive design where the sample size in the new trial can be adaptively increased. In the body of the program, the user can specify design parameters that are used in the simulation |
BayesProp: Bayesian Clinical Trial Design for Regression Models Using Historical Data (SAS)
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