, Jun(Jessie) He [aut]
, Abraham Eyman Casey
[aut]| Title: | Sequential Multiple Assignment Randomized Trial Design |
|---|---|
| Description: | SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021) <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage. |
| Authors: | Jason Sinnwell [aut, cre]
, Jun(Jessie) He [aut]
, Abraham Eyman Casey
[aut] |
| Maintainer: | Jason Sinnwell <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.74 |
| Built: | 2024-08-21 04:45:45 UTC |
| Source: | https://github.com/cran/smartDesign |
Power Calculations Comparing two Dynamic Treatment Regimen (DTR) Trial design clinical trial calculations
powerDTR(dtr1, dtr2, pG_A1 = 0.8, pG_A2 = 0.8, alpha=0.05)powerDTR(dtr1, dtr2, pG_A1 = 0.8, pG_A2 = 0.8, alpha=0.05)
dtr1 |
an object of smartDTR class, created by function of the same name |
dtr2 |
an object of smartDTR class, created by function of the same name |
pG_A1 |
probability of response to therapy given assignment to A1 |
pG_A2 |
probability of response to therapy given assignment to A2 |
alpha |
accepted type-I error rate for power calculations |
more details on power DTR
An object of the powerDTR S3 class, with the following elements:
powerdat: |
data.frame with sens, spec, mu, sigsq and sample size, power |
Jun (Jessie) He, Aberaham Eyman-Casey, Jason P. Sinnwell, Mayo Clinic
mumat13 <- cbind(G1=c(30,35), G0=c(20,28)) varmat13 <- cbind(G1=c(100,100),G0=c(100,100)) dtr13 <- smartDTR(mu_Barm=mumat13, sigsq_Barm=varmat13, Barm=c(1,3), nsubject=252, pG_A1=0.8) mumat24 <- cbind(G1=c(25,32), G0=c(18,23)) varmat24 <- cbind(G1=c(100,100),G0=c(100,100)) dtr24 <- smartDTR(mu_Barm=mumat24, sigsq_Barm=varmat24, Barm=c(2,4), nsubject=252, pG_A1=0.8, pG_A2=0.8) pdtr13vs24 <- powerDTR(dtr13, dtr24) print(pdtr13vs24) ## plot(pdtr13vs24)mumat13 <- cbind(G1=c(30,35), G0=c(20,28)) varmat13 <- cbind(G1=c(100,100),G0=c(100,100)) dtr13 <- smartDTR(mu_Barm=mumat13, sigsq_Barm=varmat13, Barm=c(1,3), nsubject=252, pG_A1=0.8) mumat24 <- cbind(G1=c(25,32), G0=c(18,23)) varmat24 <- cbind(G1=c(100,100),G0=c(100,100)) dtr24 <- smartDTR(mu_Barm=mumat24, sigsq_Barm=varmat24, Barm=c(2,4), nsubject=252, pG_A1=0.8, pG_A2=0.8) pdtr13vs24 <- powerDTR(dtr13, dtr24) print(pdtr13vs24) ## plot(pdtr13vs24)
Power Calculations Comparing two Single Sequential Treatment Treatment (SST) Trial design clinical trial calculations
powerSST(sst1, sst2, pG_A1 = 0.8, pG_A2 = 0.8, alpha=0.05)powerSST(sst1, sst2, pG_A1 = 0.8, pG_A2 = 0.8, alpha=0.05)
sst1 |
an object of smartSST class, created by function of the same name |
sst2 |
an object of smartSST class, created by function of the same name |
pG_A1 |
probability of response to therapy given assignment to A1 |
pG_A2 |
probability of response to therapy given assignment to A2 |
alpha |
accepted type-I error rate for power calculations |
more details to come
An object of the powerSST S3 class, with the following elements:
powerdat: |
data.frame with sens, spec, mu, sigsq and sample size, power |
Jun (Jessie) He, Aberaham Eyman-Casey, Jason P. Sinnwell, Mayo Clinic
sst1 <- smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=16,G0=16), Barm=1, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) sst2 <- smartSST(mu_Barm=c(G1=20, G0=30), sigsq_Barm=c(G1=16,G0=16), Barm=2, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) psst12 <- powerSST(sst1, sst2) print(psst12) ## plot(psst12)sst1 <- smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=16,G0=16), Barm=1, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) sst2 <- smartSST(mu_Barm=c(G1=20, G0=30), sigsq_Barm=c(G1=16,G0=16), Barm=2, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) psst12 <- powerSST(sst1, sst2) print(psst12) ## plot(psst12)
Dynamic Treatment Regimen (DTR) Trial design clinical trial calculations
smartDTR(mu_Barm=cbind(G1=c(30,25), G0=c(20,20)), sigsq_Barm=cbind(G1=c(100,100), G0=c(100,100)), nsubject=500, Barm=c(1,3), type="continuous", sens=seq(0.5,1, by=0.1), spec=seq(0.5, 1, by=0.1), pG_A1 = 0.8, pG_A2 = 0.8, pran_A1 = 0.5, pran_Barm = c(0.5, 0.5))smartDTR(mu_Barm=cbind(G1=c(30,25), G0=c(20,20)), sigsq_Barm=cbind(G1=c(100,100), G0=c(100,100)), nsubject=500, Barm=c(1,3), type="continuous", sens=seq(0.5,1, by=0.1), spec=seq(0.5, 1, by=0.1), pG_A1 = 0.8, pG_A2 = 0.8, pran_A1 = 0.5, pran_Barm = c(0.5, 0.5))
mu_Barm |
matrix of two named vectors of the means for the two B arms (columns) for the smart DTR trial, with rows as 'G1' and 'G0' |
sigsq_Barm |
matrix of two named vectors of the variances (sigma-squared) for the two Blevels (columns) for the smart DTR trial, with rows as 'G1' and 'G0' |
nsubject |
total sample size for the trial |
Barm |
for the second phase of the trial, the 'B' levels for which the DTR means/variances apply |
type |
trial response variable type; only continuous is implemented currently |
sens |
range of sensitivity for smart SST calculations; (0,1] |
spec |
range of specificity for smart SST calculations; (0,1] |
pG_A1 |
probability of response to therapy given assignment to A1 |
pG_A2 |
probability of response to therapy given assignment to A2 |
pran_A1 |
probability of random assignment to A1 |
pran_Barm |
probability of assignment to Barms |
see details in the reference
An object of the smartDTR S3 class, with the following elements:
dtrdat: |
data.frame with sens, spec, mu, sigsq and sample size (n) |
sst1: |
smartSST object from the first Barm |
sst2: |
smartSST object from the second Barm |
true_mumix: |
true mu mixture |
true_sigmix: |
true sigma mixture |
mu_Barm, sigsq_Barm, Barm:
|
input B-arm, mu, and sigsq for DTR |
Jun (Jessie) He, Aberaham Eyman-Casey, Jason P. Sinnwell, Mayo Clinic
Jun He, Donna K. McClish & Roy T. Sabo (2021) Evaluating Misclassification Effects on Single Sequential Treatment in Sequential Multiple Assignment Randomized Trial (SMART) Designs, Statistics in Biopharmaceutical Research, DOI: 10.1080/19466315.2021.1883472
mumat13 <- cbind(G1=c(30,35), G0=c(20,28)) varmat13 <- cbind(G1=c(100,100),G0=c(100,100)) dtr13 <- smartDTR(mu_Barm=mumat13, sigsq_Barm=varmat13, Barm=c(1,3), nsubject=252, pG_A1=0.8) print(dtr13)mumat13 <- cbind(G1=c(30,35), G0=c(20,28)) varmat13 <- cbind(G1=c(100,100),G0=c(100,100)) dtr13 <- smartDTR(mu_Barm=mumat13, sigsq_Barm=varmat13, Barm=c(1,3), nsubject=252, pG_A1=0.8) print(dtr13)
Single Sequential Trial design clinical trial calculations
smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=100, G0=100), nsubject=500, Barm=1, type="continuous", sens=seq(0.5,1, by=0.1), spec=seq(0.5, 1, by=0.1), pG_A1 = 0.8, pG_A2=0.8, pran_A1 = 0.5, pran_Barm = 0.5)smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=100, G0=100), nsubject=500, Barm=1, type="continuous", sens=seq(0.5,1, by=0.1), spec=seq(0.5, 1, by=0.1), pG_A1 = 0.8, pG_A2=0.8, pran_A1 = 0.5, pran_Barm = 0.5)
mu_Barm |
named vector of the means for the Barm for the smart SST trial, with names 'G1' and 'G0' |
sigsq_Barm |
named vector of the variances (sigma-squared) for the Barm for the smart SST trial, with names 'G1' and 'G0' |
nsubject |
total sample size for the trial |
Barm |
for the second phase of the trial, the 'B' level for which the means/variances apply |
type |
trial response variable type; only continuous is implemented currently |
sens |
range of sensitivity for smart SST calculations; (0,1] |
spec |
range of specificity for smart SST calculations; (0,1] |
pG_A1 |
probability of response to therapy given assignment to A1 |
pG_A2 |
probability of response to therapy given assignment to A2 |
pran_A1 |
probability of random assignment to A1 |
pran_Barm |
probability of assignment to Barm |
more details on smart SST
An object of the smartSST S3 class, with the following elements:
sstdat: |
data.frame with sens, spec, mu, sigsq and sample size (n) |
mu_Barm: |
The value of mu_Barm passed to the function |
sigsq_Barm: |
The value of sigsq_Barm passed to the function |
Jun (Jessie) He, Aberaham Eyman-Casey, Jason P. Sinnwell, Mayo Clinic
Jun He, Donna K. McClish & Roy T. Sabo (2021) Evaluating Misclassification Effects on Single Sequential Treatment in Sequential Multiple Assignment Randomized Trial (SMART) Designs, Statistics in Biopharmaceutical Research, DOI: 10.1080/19466315.2021.1883472
sst1 <- smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=16,G0=16), Barm=1, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) print(sst1$sstdat, digits=2)sst1 <- smartSST(mu_Barm=c(G1=30, G0=20), sigsq_Barm=c(G1=16,G0=16), Barm=1, sens=seq(.6, 1, by=.1), spec=seq(.6, 1, by=.1), nsubject=252) print(sst1$sstdat, digits=2)