## ----setup, eval = FALSE------------------------------------------------------ # install.packages("./crm12Comb_0.1.9.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) ## ----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)