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inst/doc/OneArm2stage.R
In OneArm2stage: Phase II Single-Arm Two-Stage Designs with Time-to-Event Outcomes
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, message=F, warning=F----------------------------------------------
library(OneArm2stage)
library(survival)
library(IPDfromKM)
## ---- echo=T, eval=F----------------------------------------------------------
# Design1 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, restricted=0)
## ----eval=F-------------------------------------------------------------------
# Design1
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 22 -0.7215 46 1.5952 1.0619 5.3 40.1736 0.2353
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 34 -0.731 44 1.6257 1.6997 5.2 41.6745 0.2324
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 42.66805
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 42.78615
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 43.08680
#
## ---- echo=T, eval=F----------------------------------------------------------
# Design2 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, prStop=0.35, restricted=0)
## ----eval=F-------------------------------------------------------------------
# Design2
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 29 -0.3844 47 1.5799 1.4364 5.35 40.5985 0.3503
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 40 -0.3829 44 1.6171 1.9808 5.2 42.4619 0.3509
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 92 34 -0.3850 45 1.5988 1.6732 5.25 40.9611 0.3501 42.98055
# # 102 40 -0.3846 44 1.6171 1.9791 5.20 42.4522 0.3503 43.22610
# # 921 31 -0.3851 46 1.5884 1.5339 5.30 40.6365 0.3501 43.31825
# # 91 29 -0.3850 47 1.5799 1.4353 5.35 40.5949 0.3501 43.79745
#
#
## ----eval=F-------------------------------------------------------------------
# Design3 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, q_value=0.1, restricted=0)
#
# Design3_2 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, q_value=0.7, restricted=0)
## ----eval=F-------------------------------------------------------------------
#
# # q_value = 0.1
# Design3
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 40.75624
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 40.80249
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 41.81507
#
# # q_value = 0.7
# Design3_2
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 43.27169
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 43.60083
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 44.25208
## ----echo=F-------------------------------------------------------------------
dat1 <- read.csv(system.file("extdata", "d4_t1.csv", package = "OneArm2stage"))
dat1 <- dat1[order(dat1$Entry), c("Entry", "time", "status", "Total")] #order by entry
print(dat1)
## ----echo=F-------------------------------------------------------------------
KM1 <- survfit(Surv(time, status) ~ 1, data = dat1, conf.type = "log-log")
plot(KM1, mark.time = FALSE, main = "Kaplan-Meier survival curve", xlab = "Time", ylab = "Survival probability")
## ----echo=F-------------------------------------------------------------------
summary(KM1, times=KM1$time)
## -----------------------------------------------------------------------------
LRT(shape = 1, S0=0.72, x0=3, data=dat1)
## ----echo=F-------------------------------------------------------------------
dat2 <- read.csv(system.file("extdata", "d4.csv", package = "OneArm2stage"))
dat2 <- dat2[order(dat2$Entry), c("Entry", "time", "status", "Total")]
# order by entry
print(dat2)
## ----echo=F-------------------------------------------------------------------
KM2 <- survfit(Surv(time, status) ~ 1, data = dat2, conf.type = "log-log")
plot(KM2, mark.time = FALSE, main = "Kaplan-Meier survival curve", xlab = "Time", ylab = "Survival probability")
## ----echo=F-------------------------------------------------------------------
summary(KM2, times=KM2$time)
## -----------------------------------------------------------------------------
LRT(shape=1, S0=0.72, x0=3, data=dat2)
## ---- echo=T, eval=F----------------------------------------------------------
# Design5 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, restricted=1)
## ----eval=F-------------------------------------------------------------------
# Design5
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 36 -0.3665 60 1.6 1.7523 6 51.0914 0.357
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 37 -0.5517 58 1.6193 1.8222 5.9 51.7359 0.2906
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 120 37 -0.5625 58 1.6196 1.8066 5.90 51.7260 0.2869 54.86300
# # 88 36 -0.4531 59 1.6086 1.7515 5.95 51.2044 0.3252 55.10220
# # 232 36 -0.3643 60 1.6000 1.7556 6.00 51.0949 0.3578 55.54745
#
## ----eval=F-------------------------------------------------------------------
# Design1$param
# # shape S0 hr alpha beta rate x0 tf q_value prStop restricted
# # 1 0.72 0.459 0.05 0.2 20 3 3 0.5 0 0
#
# Design1$Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 22 -0.7215 46 1.5952 1.0619 5.3 40.1736 0.2353
#
# Design1$Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 34 -0.731 44 1.6257 1.6997 5.2 41.6745 0.2324
#
# Design1$Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 42.66805
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 42.78615
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 43.08680
#
## -----------------------------------------------------------------------------
# calculate empirical power based on optimal method, power=0.778
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.0619,
c1=-0.7215, c=1.5952, n1=22, n=46, N=10000, seed=5868)
# calculate empirical power based on minmax method, power=0.803
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.6997,
c1=-0.731, c=1.6257, n1=34, n=44, N=10000, seed=5868)
# calculate empirical power based on admissible method, power=0.789
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.2512,
c1=-0.7274, c=1.6061, n1=26, n=45, N=10000, seed=5868)
## ----eval=F-------------------------------------------------------------------
# Design5
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 36 -0.3665 60 1.6 1.7523 6 51.0914 0.357
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 37 -0.5517 58 1.6193 1.8222 5.9 51.7359 0.2906
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 120 37 -0.5625 58 1.6196 1.8066 5.90 51.7260 0.2869 54.86300
# # 88 36 -0.4531 59 1.6086 1.7515 5.95 51.2044 0.3252 55.10220
# # 232 36 -0.3643 60 1.6000 1.7556 6.00 51.0949 0.3578 55.54745
#
## -----------------------------------------------------------------------------
# calculate empirical power based on optimal method, power=0.799
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.7523,
c1=-0.3665, c=1.6, n1=36, n=60, N=10000, restricted=1, seed=5868)
# calculate empirical power based on minmax method, power=0.796
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.8222,
c1=-0.5517, c=1.6193, n1=37, n=58, N=10000, restricted=1, seed=5868)
# calculate empirical power based on admissible method, power=0.796
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.8066,
c1=-0.5625, c=1.6196, n1=37, n=58, N=10000, restricted=1, seed=5868)
## ----eval=F-------------------------------------------------------------------
# ## First, we take a screenshot of the plot and save it as a .png file. In this example it is named as "wang2018.png".
#
# ## Next, create a path directing to the .png file.
# ## e.g. path <-"./vignettes/wang2018.png"
#
# ## Last, type the following code in console, and follow the prompts to extract data points by clicking your mouse on the plot.
# ## df <- getpoints(path, 0, 24, 0, 100) # 0,24,0,100 are the ranges of x and y-axes in the image
#
## -----------------------------------------------------------------------------
# a sample dataset that we already extracted from Wang et al, 2018.
df<- read.csv(system.file("extdata", "df.csv", package = "OneArm2stage"))
# risk time points
trisk <- c(0,2,4,6,8,10,12,14,16,18,20,22,24)
# number of patients at risk at each risk time point
nrisk.radio <- c(124,120,115,110,107,104,103,95,46,18,11,8,0)
# Preprocess the raw coordinates into an proper format for reco""nstruct IPD
pre_radio <- preprocess(dat=df, trisk=trisk, nrisk=nrisk.radio,totalpts=NULL,maxy=100)
#Reconstruct IPD
est_radio <- getIPD(prep=pre_radio,armID=0,tot.events=NULL)
## -----------------------------------------------------------------------------
plot_ex5 <- survreport(ipd1=est_radio$IPD,ipd2=NULL,arms=1,interval=2,s=c(0.75,0.5,0.25),showplots=F)
plot_ex5$arm1$survprob
## -----------------------------------------------------------------------------
ipd <- est_radio$IPD
dat3 <- as.data.frame(cbind(rep(0, nrow(ipd)),ipd$time, ipd$status))
colnames(dat3) <- c("Entry", "Time", "Cens")
## -----------------------------------------------------------------------------
modelWB<- FitDat(dat3)
#AIC
modelWB$AIC
modelWB$parameter.estimates
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OneArm2stage documentation built on Oct. 10, 2023, 1:08 a.m.
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, message=F, warning=F----------------------------------------------
library(OneArm2stage)
library(survival)
library(IPDfromKM)
## ---- echo=T, eval=F----------------------------------------------------------
# Design1 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, restricted=0)
## ----eval=F-------------------------------------------------------------------
# Design1
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 22 -0.7215 46 1.5952 1.0619 5.3 40.1736 0.2353
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 34 -0.731 44 1.6257 1.6997 5.2 41.6745 0.2324
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 42.66805
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 42.78615
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 43.08680
#
## ---- echo=T, eval=F----------------------------------------------------------
# Design2 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, prStop=0.35, restricted=0)
## ----eval=F-------------------------------------------------------------------
# Design2
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 29 -0.3844 47 1.5799 1.4364 5.35 40.5985 0.3503
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 40 -0.3829 44 1.6171 1.9808 5.2 42.4619 0.3509
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 92 34 -0.3850 45 1.5988 1.6732 5.25 40.9611 0.3501 42.98055
# # 102 40 -0.3846 44 1.6171 1.9791 5.20 42.4522 0.3503 43.22610
# # 921 31 -0.3851 46 1.5884 1.5339 5.30 40.6365 0.3501 43.31825
# # 91 29 -0.3850 47 1.5799 1.4353 5.35 40.5949 0.3501 43.79745
#
#
## ----eval=F-------------------------------------------------------------------
# Design3 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, q_value=0.1, restricted=0)
#
# Design3_2 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, q_value=0.7, restricted=0)
## ----eval=F-------------------------------------------------------------------
#
# # q_value = 0.1
# Design3
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 40.75624
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 40.80249
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 41.81507
#
# # q_value = 0.7
# Design3_2
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 43.27169
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 43.60083
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 44.25208
## ----echo=F-------------------------------------------------------------------
dat1 <- read.csv(system.file("extdata", "d4_t1.csv", package = "OneArm2stage"))
dat1 <- dat1[order(dat1$Entry), c("Entry", "time", "status", "Total")] #order by entry
print(dat1)
## ----echo=F-------------------------------------------------------------------
KM1 <- survfit(Surv(time, status) ~ 1, data = dat1, conf.type = "log-log")
plot(KM1, mark.time = FALSE, main = "Kaplan-Meier survival curve", xlab = "Time", ylab = "Survival probability")
## ----echo=F-------------------------------------------------------------------
summary(KM1, times=KM1$time)
## -----------------------------------------------------------------------------
LRT(shape = 1, S0=0.72, x0=3, data=dat1)
## ----echo=F-------------------------------------------------------------------
dat2 <- read.csv(system.file("extdata", "d4.csv", package = "OneArm2stage"))
dat2 <- dat2[order(dat2$Entry), c("Entry", "time", "status", "Total")]
# order by entry
print(dat2)
## ----echo=F-------------------------------------------------------------------
KM2 <- survfit(Surv(time, status) ~ 1, data = dat2, conf.type = "log-log")
plot(KM2, mark.time = FALSE, main = "Kaplan-Meier survival curve", xlab = "Time", ylab = "Survival probability")
## ----echo=F-------------------------------------------------------------------
summary(KM2, times=KM2$time)
## -----------------------------------------------------------------------------
LRT(shape=1, S0=0.72, x0=3, data=dat2)
## ---- echo=T, eval=F----------------------------------------------------------
# Design5 <- phase2.TTE(shape=1,S0=0.72,x0=3,hr=0.459,tf=3,rate=20,alpha=0.05,
# beta=0.2, restricted=1)
## ----eval=F-------------------------------------------------------------------
# Design5
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 36 -0.3665 60 1.6 1.7523 6 51.0914 0.357
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 37 -0.5517 58 1.6193 1.8222 5.9 51.7359 0.2906
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 120 37 -0.5625 58 1.6196 1.8066 5.90 51.7260 0.2869 54.86300
# # 88 36 -0.4531 59 1.6086 1.7515 5.95 51.2044 0.3252 55.10220
# # 232 36 -0.3643 60 1.6000 1.7556 6.00 51.0949 0.3578 55.54745
#
## ----eval=F-------------------------------------------------------------------
# Design1$param
# # shape S0 hr alpha beta rate x0 tf q_value prStop restricted
# # 1 0.72 0.459 0.05 0.2 20 3 3 0.5 0 0
#
# Design1$Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 22 -0.7215 46 1.5952 1.0619 5.3 40.1736 0.2353
#
# Design1$Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 34 -0.731 44 1.6257 1.6997 5.2 41.6745 0.2324
#
# Design1$Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 88 26 -0.7274 45 1.6061 1.2512 5.25 40.3361 0.2335 42.66805
# # 98 29 -1.0069 44 1.6293 1.4269 5.20 41.5723 0.1570 42.78615
# # 108 21 -0.7393 46 1.5952 1.0326 5.30 40.1736 0.2298 43.08680
#
## -----------------------------------------------------------------------------
# calculate empirical power based on optimal method, power=0.778
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.0619,
c1=-0.7215, c=1.5952, n1=22, n=46, N=10000, seed=5868)
# calculate empirical power based on minmax method, power=0.803
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.6997,
c1=-0.731, c=1.6257, n1=34, n=44, N=10000, seed=5868)
# calculate empirical power based on admissible method, power=0.789
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.2512,
c1=-0.7274, c=1.6061, n1=26, n=45, N=10000, seed=5868)
## ----eval=F-------------------------------------------------------------------
# Design5
# # $Two_stage_Optimal
# # n1 c1 n c t1 MTSL ES PS
# # 1 36 -0.3665 60 1.6 1.7523 6 51.0914 0.357
# #
# # $Two_stage_minmax
# # n1 c1 n c t1 MTSL ES PS
# # 1 37 -0.5517 58 1.6193 1.8222 5.9 51.7359 0.2906
# #
# # $Two_stage_Admissible
# # n1 c1 n c t1 MTSL ES PS Rho
# # 120 37 -0.5625 58 1.6196 1.8066 5.90 51.7260 0.2869 54.86300
# # 88 36 -0.4531 59 1.6086 1.7515 5.95 51.2044 0.3252 55.10220
# # 232 36 -0.3643 60 1.6000 1.7556 6.00 51.0949 0.3578 55.54745
#
## -----------------------------------------------------------------------------
# calculate empirical power based on optimal method, power=0.799
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.7523,
c1=-0.3665, c=1.6, n1=36, n=60, N=10000, restricted=1, seed=5868)
# calculate empirical power based on minmax method, power=0.796
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.8222,
c1=-0.5517, c=1.6193, n1=37, n=58, N=10000, restricted=1, seed=5868)
# calculate empirical power based on admissible method, power=0.796
Sim(shape=1, S0=0.72, S1=0.72^(0.459), x0=3, tf=3, rate=20, t1=1.8066,
c1=-0.5625, c=1.6196, n1=37, n=58, N=10000, restricted=1, seed=5868)
## ----eval=F-------------------------------------------------------------------
# ## First, we take a screenshot of the plot and save it as a .png file. In this example it is named as "wang2018.png".
#
# ## Next, create a path directing to the .png file.
# ## e.g. path <-"./vignettes/wang2018.png"
#
# ## Last, type the following code in console, and follow the prompts to extract data points by clicking your mouse on the plot.
# ## df <- getpoints(path, 0, 24, 0, 100) # 0,24,0,100 are the ranges of x and y-axes in the image
#
## -----------------------------------------------------------------------------
# a sample dataset that we already extracted from Wang et al, 2018.
df<- read.csv(system.file("extdata", "df.csv", package = "OneArm2stage"))
# risk time points
trisk <- c(0,2,4,6,8,10,12,14,16,18,20,22,24)
# number of patients at risk at each risk time point
nrisk.radio <- c(124,120,115,110,107,104,103,95,46,18,11,8,0)
# Preprocess the raw coordinates into an proper format for reco""nstruct IPD
pre_radio <- preprocess(dat=df, trisk=trisk, nrisk=nrisk.radio,totalpts=NULL,maxy=100)
#Reconstruct IPD
est_radio <- getIPD(prep=pre_radio,armID=0,tot.events=NULL)
## -----------------------------------------------------------------------------
plot_ex5 <- survreport(ipd1=est_radio$IPD,ipd2=NULL,arms=1,interval=2,s=c(0.75,0.5,0.25),showplots=F)
plot_ex5$arm1$survprob
## -----------------------------------------------------------------------------
ipd <- est_radio$IPD
dat3 <- as.data.frame(cbind(rep(0, nrow(ipd)),ipd$time, ipd$status))
colnames(dat3) <- c("Entry", "Time", "Cens")
## -----------------------------------------------------------------------------
modelWB<- FitDat(dat3)
#AIC
modelWB$AIC
modelWB$parameter.estimates
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