## ----setup, eval = FALSE------------------------------------------------------
# install.packages("./crm12Comb_0.1.11.tar.gz", repos = NULL, type = "source")
# library(crm12Comb)
# help(package="crm12Comb")
## ----eval=FALSE---------------------------------------------------------------
# ### main function, please use this function to run simulations
# ?SIM_phase_I_II
#
# ### helper functions need to run before simulations (pre-defined settings)
# ?priorSkeletons
#
# ### helper functions included in the main function SIM_phase_I_II()
# ?rBin2Corr
# ?ger_ordering
# ?toxicity_est
# ?efficacy_est
# ?randomization_phase
# ?maximization_phase
## ----table1, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'--------
tabl1 <- "
| | | Scenario | | |
|----------|-------------|-------------|-------------|-------------|
| | | Doses of A | | |
|Doses of B| 1 | 2 | 3 | 4 |
|4 | (0.20,0.24) | **(0.24,0.35)** | (0.35,0.45) | (0.40,0.50) |
|3 | (0.12,0.18) | (0.16,0.22) | **(0.22,0.35)** | **(0.25,0.40)** |
|2 | (0.06,0.10) | (0.10,0.15) | (0.14,0.25) | **(0.20,0.35)** |
|1 | (0.02,0.05) | (0.04,0.10) | (0.08,0.15) | **(0.12,0.32)** |
"
cat(tabl1)
## ----eval = TRUE--------------------------------------------------------------
library(crm12Comb)
# generate skeletons
DLT_skeleton <- priorSkeletons(updelta=0.025, target=0.3, npos=10, ndose=16,
model = "empiric", prior = "normal", beta_mean=0)
print(paste0("DLT skeleton is: ", paste(round(DLT_skeleton,3), collapse=", ")))
Efficacy_skeleton <- priorSkeletons(updelta=0.025, target=0.5, npos=10, ndose=16,
model = "empiric", prior = "normal", beta_mean=0)
print(paste0("Efficacy skeleton is: ", paste(round(Efficacy_skeleton,3), collapse=", ")))
## ----eval = TRUE--------------------------------------------------------------
scenario <- matrix(c(0.02, 0.05,
0.04, 0.10,
0.08, 0.15,
0.12, 0.32,
0.06, 0.10,
0.10, 0.15,
0.14, 0.25,
0.20, 0.35,
0.12, 0.18,
0.16, 0.22,
0.22, 0.35,
0.25, 0.40,
0.20, 0.24,
0.24, 0.35,
0.35, 0.45,
0.40, 0.50), ncol=2, byrow = TRUE)
# simulate 100 trials under the same model and prior distribution
simRes <- SIM_phase_I_II(nsim=100, Nmax=40, DoseComb=scenario, input_doseComb_forMat=c(4,4),
input_type_forMat="matrix", input_Nphase=20,
input_DLT_skeleton=DLT_skeleton,
input_efficacy_skeleton=Efficacy_skeleton,
input_DLT_thresh=0.3, input_efficacy_thresh=0.3,
input_cohortsize=1, input_corr=0,
input_early_stopping_safety_thresh=0.33,
input_early_stopping_futility_thresh=0.2,
input_model="empiric", input_para_prior="normal",
input_beta_mean=0, input_beta_sd=sqrt(1.34),
input_theta_mean=0, input_theta_sd=sqrt(1.34),
random_seed=123)
print(paste0("Probability of recommending safe/ineffective combinations as ODC is ", simRes$prob_safe))
print(paste0("Probability of recommending target combinations as ODC is ", simRes$prob_target))
print(paste0("Probability of recommending overly toxic combinations as ODC is ", simRes$prob_toxic))
print(paste0("Mean # of patients enrolled is ", simRes$mean_SS))
print(paste0("Proportion of patients allocated to true ODC(s) is ", simRes$mean_ODC))
print(paste0("Proportion stopped for safety is ", simRes$prob_stop_safety))
print(paste0("Proportion stopped for futility is ", simRes$prob_stop_futility))
print(paste0("Observed DLT rate is ", simRes$mean_DLT))
print(paste0("Observed response rate is ", simRes$mean_ORR))
## ----eval = TRUE, fig.width=10, fig.height=5----------------------------------
# generate plots of patient enrollment of the first trial
enroll_patient_plot(simRes$datALL[[3]])
## ----eval = TRUE, fig.width=8, fig.height=6-----------------------------------
# generate plots of patient allocations by dose levels of the first trial
patient_allocation_plot(simRes$datALL[[3]])
## ----eval = TRUE, fig.width=8, fig.height=6-----------------------------------
# generate plots of ODC selections among all trials
ODC_plot(simRes)
## ----eval = TRUE--------------------------------------------------------------
set.seed(123)
currDat <- data.frame(sample(1:6, 6, replace=TRUE), rbinom(6, 1, 0.2), rbinom(6, 1, 0.5))
names(currDat) <- c("DoseLevel", "DLT", "ORR")
currDat
## ----eval = TRUE--------------------------------------------------------------
orderings <- get_ordering(doseComb_forMat=c(4,4), type_forMat="matrix")
orderings
## ----eval = TRUE--------------------------------------------------------------
DLT <- lapply(orderings, function(or){DLT_skeleton[order(or)]})
ORR <- lapply(orderings, function(or){Efficacy_skeleton[order(or)]})
lapply(DLT, function (x) round(x, 3))
lapply(ORR, function (x) round(x, 3))
## ----eval = TRUE--------------------------------------------------------------
tox <- toxicity_est(Dat=currDat, I=16, M=6,
M_prob=rep(1/6,6), DLT_skeleton=DLT, DLT_thresh=0.3,
model="empiric", para_prior="normal", beta_mean=0,
beta_sd=1, intcpt_lgst1=NULL, beta_shape=NULL,
beta_inverse_scale=NULL, alpha_mean=NULL,
alpha_sd=NULL, alpha_shape=NULL,
alpha_inverse_scale=NULL, seed=23)
tox
## ----eval = TRUE--------------------------------------------------------------
eff <- efficacy_est(Dat=currDat, AR=tox$AR, I=16, K=6,
K_prob=rep(1/6,6), efficacy_skeleton=ORR, Nphas=20,
model="empiric", para_prior="normal", theta_mean=0,
theta_sd=1, theta_intcpt_lgst1=NULL, theta_shape=NULL,
theta_inverse_scale=NULL, alphaT_mean=NULL,
alphaT_sd=NULL, alphaT_shape=NULL,
alphaT_inverse_scale=NULL, seed=23, seed_rand=23,
seed_max=23)
eff
## ----table2, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'--------
tabl2 <- "
| | | | True (toxicity, efficacy) probabilities ||
|----------|----------|------------------|------------------|------------------|
| | Doses of | | Doses of B | |
| Scenario | A | 1 | 2 | 3 |
| 1 | 3 | (0.08, 0.15) | (0.10, 0.20) | **(0.18, 0.40)** |
| | 2 | (0.04, 0.10) | (0.06, 0.16) | (0.08, 0.20) |
| | 1 | (0.02, 0.05) | (0.04, 0.10) | (0.06, 0.15) |
| 2 | 3 | (0.16, 0.20) | **(0.25, 0.35)** | (0.35, 0.50) |
| | 2 | (0.10, 0.10) | (0.14, 0.25) | **(0.20, 0.40)** |
| | 1 | (0.06, 0.05) | (0.08, 0.10) | (0.12, 0.20) |
| 3 | 3 | **(0.24, 0.40)** | (0.33, 0.50) | (0.40, 0.60) |
| | 2 | (0.16, 0.20) | **(0.22, 0.40)** | (0.35, 0.50) |
| | 1 | (0.08, 0.10) | (0.14, 0.25) | **(0.20, 0.35)** |
| 4 | 3 | (0.33, 0.50) | (0.40, 0.60) | (0.55, 0.70) |
| | 2 | **(0.18, 0.35)** | **(0.25, 0.45)** | (0.42, 0.55) |
| | 1 | (0.12, 0.20) | **(0.20, 0.40)** | (0.35, 0.50) |
| 5 | 3 | (0.45, 0.55) | (0.55, 0.65) | (0.75, 0.75) |
| | 2 | **(0.20, 0.36)** | (0.35, 0.49) | (0.40, 0.62) |
| | 1 | (0.15, 0.20) | **(0.20, 0.35)** | **(0.25, 0.50)** |
| 6 | 3 | (0.65, 0.60) | (0.80, 0.65) | (0.85, 0.70) |
| | 2 | (0.55, 0.55) | (0.70, 0.60) | (0.75, 0.65) |
| | 1 | (0.50, 0.50) | (0.55, 0.55) | (0.65, 0.60) |
"
cat(tabl2)
## ----eval = FALSE-------------------------------------------------------------
# scenario1 <- matrix(c(0.02, 0.05,
# 0.04, 0.10,
# 0.06, 0.15,
# 0.04, 0.10,
# 0.06, 0.16,
# 0.08, 0.20,
# 0.08, 0.15,
# 0.10, 0.20,
# 0.18, 0.40), ncol=2, byrow = TRUE)
## ----eval = FALSE-------------------------------------------------------------
# orderings <- function(DLT1, DLT2, ORR1, ORR2){
# input_Nphase <- c(10, 20, 30)
# input_corr <- c(0, -2.049, 0.814)
# input_N <- c(40, 50, 60)
# DLTs <- list(DLT1, DLT2)
# ORRs <- list(ORR1, ORR2)
#
# conds <- list()
# i <- 1
# conds <- list()
# i <- 1
# for (s in 1:2){
# for (n in 1:3){
# for (np in 1:3){
# for (c in 1:3){
# conds[[i]] <- list(DLT=DLTs[[s]], ORR=ORRs[[s]], sklnum = s,
# N=input_N[n], Nphase=input_Nphase[np],
# corr=input_corr[c])
# i <- i+1
# }
# }
# }
# }
#
# return(conds)
# }
#
# SC <- list(scenario1, scenario2, scenario3, scenario4, scenario5, scenario6)
## ----eval = FALSE-------------------------------------------------------------
# output <- data.frame(Scenario = double(), Skeleton = double(),
# N = double(), nR = double(), corr = double(), safe = double(),
# target = double(), toxic = double(), avgSS = double(),
# prop_ODC = double(), stop_safety = double(), stop_futility = double(),
# o_DLT = double(), o_ORR = double())
#
# colnames(output) <- c("Scenario", "Skeleton", "N", "nR", "corr",
# "Probability of recommending safe/ineffective combinations as ODC",
# "Probability of recommending target combinations as ODC",
# "Probability of recommending overly toxic combinations as ODC",
# "Mean # of patients enrolled", "Proportion of patients allocated to true ODC(s)",
# "Proportion stopped for safety", "Proportion stopped for futility",
# "Observed DLT rate", "Observed response rate")
## ----eval = FALSE-------------------------------------------------------------
# # empiric, normal prior
# DLT_skeleton1 <- priorSkeletons(updelta=0.045, target=0.3, npos=5, ndose=9,
# model = "empiric", prior = "normal", beta_mean=0)
# DLT_skeleton2 <- priorSkeletons(updelta=0.06, target=0.3, npos=4, ndose=9,
# model = "empiric", prior = "normal", beta_mean=0)
# Efficacy_skeleton1 <- priorSkeletons(updelta=0.045, target=0.5, npos=5, ndose=9,
# model = "empiric", prior = "normal", beta_mean=0)
# Efficacy_skeleton2 <- priorSkeletons(updelta=0.06, target=0.5, npos=4, ndose=9,
# model = "empiric", prior = "normal", beta_mean=0)
#
# conds <- orderings(DLT1=DLT_skeleton1, DLT2=DLT_skeleton2,
# ORR1=Efficacy_skeleton1, ORR2=Efficacy_skeleton2)
## ----eval = FALSE-------------------------------------------------------------
# for (s in 1:length(SC)){
# for (c in 1:length(conds)){
# print(paste0("Scenario=", s, ", skeleton=", conds[[c]]$sklnum,
# ", N=", conds[[c]]$N, ", nR=", conds[[c]]$Nphase,
# ", corr=", conds[[c]]$corr))
# curr = SIM_phase_I_II(nsim=1000, Nmax=conds[[c]]$N, DoseComb=SC[[s]],
# input_doseComb_forMat=c(3,3),
# input_type_forMat="matrix",
# input_Nphase=conds[[c]]$Nphase,
# input_DLT_skeleton=conds[[c]]$DLT,
# input_efficacy_skeleton=conds[[c]]$ORR,
# input_DLT_thresh=0.3, input_efficacy_thresh=0.3,
# input_cohortsize=1, input_corr=conds[[c]]$corr,
# input_early_stopping_safety_thresh=0.33,
# input_early_stopping_futility_thresh=0.2,
# input_model="empiric", input_para_prior="normal",
# input_beta_mean=0, input_beta_sd=sqrt(1.34),
# input_theta_mean=0, input_theta_sd=sqrt(1.34),
# random_seed=42)
# currTmp = data.frame(s, conds[[c]]$sklnum, conds[[c]]$N, conds[[c]]$Nphase, conds[[c]]$corr,
# curr$prob_safe, curr$prob_target, curr$prob_toxic, curr$mean_SS, curr$mean_ODC,
# curr$prob_stop_safety, curr$prob_stop_futility, curr$mean_DLT, curr$mean_ORR)
# output = rbind(output, currTmp)
# }
# }
## ----load-data, echo=TRUE, results='hide'-------------------------------------
data(examples_results, package = "crm12Comb")
## ----eval = TRUE, fig.width=8, fig.height=6-----------------------------------
sample_plot(examples_results, outcome = "prob_target",
outname = "Probability of ODC as target combinations",
N = 40, nR = NULL, Skeleton = 1, corr = 0)
## ----eval = TRUE, fig.width=8, fig.height=6-----------------------------------
sample_plot(examples_results, outcome = "mean_ODC",
outname = "Proportion of patients allocated to true ODC(s)",
N = 60, nR = 30, Skeleton = 2, corr = NULL)