SMARTbayesR: SMARTbayesR: A package for Bayesian computation of optimal...
In SMARTbayesR: Bayesian Set of Best Dynamic Treatment Regimes and Sample Size in SMARTs for Binary Outcomes
Description
SMARTbayesR functions
Description
The SMARTbayesR package allows computation of a set of optimal embedded dynamic treatment regimes for a SMART.
Furthermore, it allows power to be calculated for sample size determination.
SMARTbayesR functions
SimDesign1 simulates a design-1 type SMART
PosteriorTrtSeqProb draws from the posterior of the probabilities of response for each embedded treatment sequence and stage-1 response probabilities.
PosteriorEDTRProbs converts treatment sequence end of study response probabilities, stage-1 response probabilities into end of study embedded dynamic treatment regime response probabilities.
MCBUpperLimits calculates simultaneous credible intervals which determines the set of optimal dynamic treatment regimes
LogOR computes the log-OR between each embedded dynamic treatment regime and the best.
LogRR computes the log-RR between each embedded dynamic treatment regime and the best.
RD computes the risk difference between each embedded dynamic treatment regime and the best.
PowerBayesian computes the power in a SMART to exclude embedded dynamic treatment regimes inferior to the best by a specified amount.
Please see Artman (2020) <arXiv:2008.02341> for details about the methodology.
SMARTbayesR documentation built on Oct. 1, 2021, 1:06 a.m.
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Description SMARTbayesR functions
Description
The SMARTbayesR package allows computation of a set of optimal embedded dynamic treatment regimes for a SMART. Furthermore, it allows power to be calculated for sample size determination.
SMARTbayesR functions
SimDesign1 simulates a design-1 type SMART
PosteriorTrtSeqProb draws from the posterior of the probabilities of response for each embedded treatment sequence and stage-1 response probabilities.
PosteriorEDTRProbs converts treatment sequence end of study response probabilities, stage-1 response probabilities into end of study embedded dynamic treatment regime response probabilities.
MCBUpperLimits calculates simultaneous credible intervals which determines the set of optimal dynamic treatment regimes
LogOR computes the log-OR between each embedded dynamic treatment regime and the best.
LogRR computes the log-RR between each embedded dynamic treatment regime and the best.
RD computes the risk difference between each embedded dynamic treatment regime and the best.
PowerBayesian computes the power in a SMART to exclude embedded dynamic treatment regimes inferior to the best by a specified amount.
Please see Artman (2020) <arXiv:2008.02341> for details about the methodology.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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