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R package for finding optimal three-outcome clinical trial designs.
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LICTR/tout
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The goal of tout is to optimise the design ofthree-outcomeclinical trials by determining their sample size and progressioncriteria.
Install the released version of tout from CRAN:
install.packages("tout")Or you can install the development version of tout fromGitHub with:
# install.packages("devtools")devtools::install_github("DTWilson/tout")
Consider a pilot trial which will measure the proportion of patients, inthe intervention arm, who adhere to the intervention. We want to usethis estimate of the probability of adherence (denoted
Let the null and alternative hypotheses betout_design() function:
library(tout)#> Warning: package 'tout' was built under R version 4.5.2design<- tout_design(rho_0=0.5,rho_1=0.7,alpha_nom=0.05,beta_nom=0.2,gamma_nom=0.6)design#> Three-outcome design#>#> Sample size: 57#> Decision thresholds: 33 38#>#> alpha = 0.04808966#> beta = 0.186012#> gamma = 0.5433195#>#> Hypotheses: 0.5 (null), 0.7 (alternative)#> Modification effect range: 0 0#> Error probability following an intermediate result: 0.5 0.5
Plotting the returned object gives an illustration of the samplingdistribution of the estimated adherence probability under eachhypothesis, alongside the optimal progression criteria and the decisionsthey will lead to:
plot(design)The same function can be used when we anticipate making some adjustmentsfollowing a pause outcome (see thetau argument); when assumingparticular probabilities of making correct decisions following a pauseoutcome (see theeta argument); and when the outcome is continuousrather than binary (see thesigma argument). Further details can befound in the vignette and in the associated manuscript.
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R package for finding optimal three-outcome clinical trial designs.
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