The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
library(SMARTbayesR)This R package provides functions for power analysis and the construction of the set of best embedded dynamic treatment regimes (EDTR) using Bayesian multiple comparison with the best (MCB) methodology. This entails the construction of simultaneous one-sided upper credible intervals for the difference between each EDTR’s summary statistic and the best EDTRs. It is designed for binary outcomes in sequential, multiple assignment, randomized trials.
We extend frequentist methods to the Bayesian setting. In particular, one may determine what sample size to enroll in order to achieve a specified power, where power is the probability of excluding inferior embedded dynamic treatment regimes from the set of best.
The set of best consists of EDTRs which are not significantly inferior to the optimal or best embedded dynamic treatment regime (Artman et al. 2020).
The best EDTR is included in the set of best with probability at least \(1-\alpha\).
In this vignette, we outline how the functions may be used together to achieve these goals.
First, run \(\texttt{SimDesign1}\) to simulate a design-1 SMART.
Then, apply \(\texttt{PosteriorTrtSeqProb}\) to sample from the posterior of the probabilities of response at each stage.
To obtain draws from the probabilities of response for each of the embedded dynamic treatment regimes (EDTRs), run \(\texttt{PosteriorEDTRProbs}\).
In order to construct the set of best (or equivalently, upper credible interval limits), run \(\texttt{MCBUpperLimits}\).
set.seed(23856)
dat <- SimDesign1(sample_size = 250,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7),
stage_one_trt_one_response_prob = 0.7,
stage_one_trt_two_response_prob = 0.4)set.seed(39864)
posterior_trt_seq_draws <- PosteriorTrtSeqProb(niter = 10000,
dat,
design = "design-1")Convert draws from each treatment sequence to draws from each embedded DTR.
posterior_EDTR_draws <- PosteriorEDTRProbs(posterior_trt_seq_draws)MCBUpperLimits(thetadraws=posterior_EDTR_draws,
alpha=0.05,
design="design-1",
type="log-OR")
#> [1] -0.8337268 -0.9920360 0.4241352 0.0000000Note that EDTRs 1 and 2 are statistically significantly inferior to EDTR 4 whereas EDTR 3 is not.
To see what the log-odds ratios are, run \(\texttt{LogOR}\). Lastly, to compute the power to exclude inferior embedded dynamic treatment regimes (EDTRs) from the set of best (or equivalently, for their upper credible interval to exclude 0), run \(\texttt{PowerBayesian}\).
Compute log-OR of a success.
LogOR(response_prob = c(0.3,0.3,0.3,0.8,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
design = "design-1")
#> EDTR 1 EDTR 2 EDTR 3 EDTR 4
#> [1,] -1.893266 -1.893266 -0.2921968 0Compute power to exclude embedded dynamic treatment regimes 1 and 2. The choice to exclude these embedded dynamic treatment regimes may be based off the values of the log-OR between each EDTR and the best.
set.seed(2364)
power1 <- PowerBayesian("design-1",
sample_size = 100,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
type="log-OR",
threshold=1)power1
#> [1] 0.8182To see what the log-relative-risk are, run \(\texttt{LogRR}\). Lastly, to compute the power to exclude inferior embedded dynamic treatment regimes (EDTRs) from the set of best (or equivalently, for their upper credible interval to exclude 0), run \(\texttt{PowerBayesian}\).
Compute log-RR of a success.
LogRR(response_prob = c(0.3,0.3,0.3,0.8,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
design = "design-1")
#> EDTR 1 EDTR 2 EDTR 3 EDTR 4
#> [1,] -0.9028677 -0.9028677 -0.08455739 0Compute power to exclude embedded dynamic treatment regimes 1 and 2. The choice to exclude these embedded dynamic treatment regimes may be based off the values of the log-RR between each EDTR and the best.
set.seed(23641)
power2 <- PowerBayesian("design-1",
sample_size = 100,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
type="log-RR",
threshold=0.8)power2
#> [1] 0.9256To see what the risk difference are, run \(\texttt{RD}\). Lastly, to compute the power to exclude inferior embedded dynamic treatment regimes (EDTRs) from the set of best (or equivalently, for their upper credible interval to exclude 0), run \(\texttt{PowerBayesian}\).
Compute RD of a success.
RD(response_prob = c(0.3,0.3,0.3,0.8,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
design = "design-1")
#> EDTR 1 EDTR 2 EDTR 3 EDTR 4
#> [1,] -0.44 -0.44 -0.06 0Compute power to exclude embedded dynamic treatment regimes 1 and 2. The choice to exclude these embedded dynamic treatment regimes may be based off the values of the log-RR between each EDTR and the best.
set.seed(236412)
power3 <- PowerBayesian("design-1",
sample_size = 100,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
type="RD",
threshold=0.3)power3
#> [1] 0.8206#General SMART To see what the log-odds ratios are, run \(\texttt{LogOR}\). Lastly, to compute the power to exclude inferior embedded dynamic treatment regimes (EDTRs) from the set of best (or equivalently, for their upper credible interval to exclude 0), run \(\texttt{PowerBayesian}\).
Compute log-OR of a success.
LogOR(response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7,0.7,0.6),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
design = "general")
#> EDTR 1 EDTR 2 EDTR 3 EDTR 4 EDTR 5 EDTR 6 EDTR 7
#> [1,] -1.791759 -1.60107 -1.510592 -1.336846 -0.1840036 -0.4418328 0
#> EDTR 8
#> [1,] -0.2719337Compute power to exclude embedded dynamic treatment regimes 1, 2, 3, 4. The choice to exclude these embedded dynamic treatment regimes may be based off the values of the log-OR between each EDTR and the best.
set.seed(23644)
power4 <- PowerBayesian("general",
sample_size = 250,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7,0.7,0.6),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
type="log-OR",
threshold=1)power4
#> [1] 0.9286To see what the log-odds ratios are, run \(\texttt{LogOR}\). Lastly, to compute the power to exclude inferior embedded dynamic treatment regimes (EDTRs) from the set of best (or equivalently, for their upper credible interval to exclude 0), run \(\texttt{PowerBayesian}\).
Compute log-OR of a success.
LogOR(response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7,0.7,0.6,0.9),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
stage_one_trt_three_response_prob = 0.7,
design = "design-3")
#> EDTR 1 EDTR 2 EDTR 3 EDTR 4 EDTR 5 EDTR 6
#> [1,] -2.305359 -2.097141 -0.9115175 -0.6631313 -0.4444945 0Compute power to exclude embedded dynamic treatment regimes 1, 2, and 3. The choice to exclude these embedded dynamic treatment regimes may be based off the values of the log-OR between each EDTR and the best.
set.seed(236445)
power5 <- PowerBayesian("design-3",
sample_size = 250,
response_prob = c(0.2,0.3,0.4,0.5,0.6,0.7,0.7,0.6,0.9),
stage_one_trt_one_response_prob = 0.6,
stage_one_trt_two_response_prob = 0.4,
stage_one_trt_three_response_prob = 0.7,
type="log-OR",
threshold=0.8)power5
#> [1] 0.4299Artman, W. J., Ertefaie, A., Lynch, K. G., & McKay, J. R. (2020). Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes. arXiv preprint arXiv:2008.02341.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.