vignettes/vignette_GAMM.R
In csetraynor/survbayes2: Bayesian Survival Models (survbayes2)
library(iclust2prog)
library(predsurv)
library(magrittr)
library(dplyr)
library(forcats)
library(tidyr)
library(purrr)
library(modelr)
library(tidybayes)
library(ggplot2)
library(ggstance)
library(ggridges)
library(rstanarm)
library(brms)
library(brmstools)
import::from(LaplacesDemon, invlogit)
devtools::document()
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
data("ic2surv")
?ic2surv
#center variables
ic2surv$NGF_centered <- ic2surv$NGF - mean(ic2surv$NGF)
ic2surv$MAP1B_centered <- ic2surv$MAP1B - mean(ic2surv$MAP1B)
ic2surv <- ic2surv %>%
dplyr::mutate(mastectomy = breast_surgery == "MASTECTOMY",
crtherapy = (chemotherapy == "YES" | radio_therapy == "YES"),
chemotherapy = chemotherapy == "YES",
radio_therapy = radio_therapy == "YES",
hormone_therapy = hormone_therapy == "YES",
kras = ifelse(NGF_centered > 0, T, F),
lef1 = ifelse(MAP1B_centered > 0, T, F) )
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
surv_form <- c("mastectomy", "chemotherapy", "age_std", "npi", "hormone_therapy")
m1.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "mastectomy", "kras:mastectomy") )
form <- as.formula( paste0(c( "status~1+offset(log_dtime)+s(time)+", paste(surv_form, collapse = "+")), collapse = ""))
#prepare long format dataset
long_x <- gen_stan_dat(x)
clinical_mode_object <- brms::brm(brms::bf(form),
data = long_ic2dat, family = poisson() )
code_clinical_mode <- brms::stancode(clinical_mode_object)
summary(m1.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
kras = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
kras = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
kras = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
kras = T
)
bc.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = bcdata, treatment = "kras-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = bchdata, treatment = "kras+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = masdata, treatment = "kras-, underwent mastectomy")
mash.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = mashdata, treatment = "kras+, underwent mastectomy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("kras", "mastectomy") )
obs.mortality$kras <- as.logical(obs.mortality$kras)
obs.mortality$mastectomy <- as.logical(obs.mortality$mastectomy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(kras & mastectomy, "kras+, underwent mastectomy", ifelse( !(kras) & (mastectomy), "kras-, underwent mastectomy", ifelse( kras & !mastectomy, "kras+, underwent breast conservation", "kras-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras+, underwent mastectomy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras+, underwent mastectomy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras-, underwent mastectomy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras-, underwent mastectomy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_kras_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
m2.map2stan <- post_surv(x = ic2surv, surv_form = c("lef1", "mastectomy", "lef1:mastectomy") )
summary(m2.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
lef1 = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
lef1 = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
lef1 = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
lef1 = T
)
bc.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = bcdata, treatment = "lef1-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = bchdata, treatment = "lef1+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = masdata, treatment = "lef1-, underwent mastectomy")
mash.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = mashdata, treatment = "lef1+, underwent mastectomy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("lef1", "mastectomy") )
obs.mortality$lef1 <- as.logical(obs.mortality$lef1)
obs.mortality$mastectomy <- as.logical(obs.mortality$mastectomy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(lef1 & mastectomy, "lef1+, underwent mastectomy", ifelse( !(lef1) & (mastectomy), "lef1-, underwent mastectomy", ifelse( lef1 & !mastectomy, "lef1+, underwent breast conservation", "lef1-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "lef1+, underwent mastectomy" | Treatment == "lef1+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1+, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "lef1+, underwent mastectomy" | Treatment == "lef1+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1+, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: lef1 + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "lef1-, underwent mastectomy" | Treatment == "lef1-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1-, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "lef1-, underwent mastectomy" | Treatment == "lef1-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1-, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: lef1 - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_lef1_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
m3.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "hormone_therapy", "kras:hormone_therapy") )
m4.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "radio_therapy", "kras:radio_therapy") )
m5.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "chemotherapy", "kras:chemotherapy") )
summary(m5.map2stan)
summary(m5.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = F,
kras = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = F,
kras = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = T,
kras = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = T,
kras = T
)
bc.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = bcdata, treatment = "kras-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = bchdata, treatment = "kras+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = masdata, treatment = "kras-, underwent chemotherapy")
mash.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = mashdata, treatment = "kras+, underwent chemotherapy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("kras", "chemotherapy") )
obs.mortality$kras <- as.logical(obs.mortality$kras)
obs.mortality$chemotherapy <- as.logical(obs.mortality$chemotherapy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(kras & chemotherapy, "kras+, underwent chemotherapy", ifelse( !(kras) & (chemotherapy), "kras-, underwent chemotherapy", ifelse( kras & !chemotherapy, "kras+, underwent breast conservation", "kras-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras+, underwent chemotherapy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent chemotherapy", "chemotherapy", "no chemotherapy") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras+, underwent chemotherapy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent chemotherapy", "chemotherapy", "no chemotherapy") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras-, underwent chemotherapy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent chemotherapy", "chemotherapy", "no chemotherapy") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras-, underwent chemotherapy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent chemotherapy", "chemotherapy", "no chemotherapy") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_kras_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
csetraynor/survbayes2 documentation built on May 30, 2019, 4:06 a.m.
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library(iclust2prog)
library(predsurv)
library(magrittr)
library(dplyr)
library(forcats)
library(tidyr)
library(purrr)
library(modelr)
library(tidybayes)
library(ggplot2)
library(ggstance)
library(ggridges)
library(rstanarm)
library(brms)
library(brmstools)
import::from(LaplacesDemon, invlogit)
devtools::document()
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
data("ic2surv")
?ic2surv
#center variables
ic2surv$NGF_centered <- ic2surv$NGF - mean(ic2surv$NGF)
ic2surv$MAP1B_centered <- ic2surv$MAP1B - mean(ic2surv$MAP1B)
ic2surv <- ic2surv %>%
dplyr::mutate(mastectomy = breast_surgery == "MASTECTOMY",
crtherapy = (chemotherapy == "YES" | radio_therapy == "YES"),
chemotherapy = chemotherapy == "YES",
radio_therapy = radio_therapy == "YES",
hormone_therapy = hormone_therapy == "YES",
kras = ifelse(NGF_centered > 0, T, F),
lef1 = ifelse(MAP1B_centered > 0, T, F) )
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
surv_form <- c("mastectomy", "chemotherapy", "age_std", "npi", "hormone_therapy")
m1.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "mastectomy", "kras:mastectomy") )
form <- as.formula( paste0(c( "status~1+offset(log_dtime)+s(time)+", paste(surv_form, collapse = "+")), collapse = ""))
#prepare long format dataset
long_x <- gen_stan_dat(x)
clinical_mode_object <- brms::brm(brms::bf(form),
data = long_ic2dat, family = poisson() )
code_clinical_mode <- brms::stancode(clinical_mode_object)
summary(m1.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
kras = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
kras = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
kras = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
kras = T
)
bc.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = bcdata, treatment = "kras-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = bchdata, treatment = "kras+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = masdata, treatment = "kras-, underwent mastectomy")
mash.plot.frame <- get_plot_new_frame(mod = m1.map2stan, newdat = mashdata, treatment = "kras+, underwent mastectomy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("kras", "mastectomy") )
obs.mortality$kras <- as.logical(obs.mortality$kras)
obs.mortality$mastectomy <- as.logical(obs.mortality$mastectomy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(kras & mastectomy, "kras+, underwent mastectomy", ifelse( !(kras) & (mastectomy), "kras-, underwent mastectomy", ifelse( kras & !mastectomy, "kras+, underwent breast conservation", "kras-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras+, underwent mastectomy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras+, underwent mastectomy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras-, underwent mastectomy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras-, underwent mastectomy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_kras_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
m2.map2stan <- post_surv(x = ic2surv, surv_form = c("lef1", "mastectomy", "lef1:mastectomy") )
summary(m2.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
lef1 = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = F,
lef1 = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
lef1 = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
mastectomy = T,
lef1 = T
)
bc.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = bcdata, treatment = "lef1-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = bchdata, treatment = "lef1+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = masdata, treatment = "lef1-, underwent mastectomy")
mash.plot.frame <- get_plot_new_frame(mod = m2.map2stan, newdat = mashdata, treatment = "lef1+, underwent mastectomy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("lef1", "mastectomy") )
obs.mortality$lef1 <- as.logical(obs.mortality$lef1)
obs.mortality$mastectomy <- as.logical(obs.mortality$mastectomy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(lef1 & mastectomy, "lef1+, underwent mastectomy", ifelse( !(lef1) & (mastectomy), "lef1-, underwent mastectomy", ifelse( lef1 & !mastectomy, "lef1+, underwent breast conservation", "lef1-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "lef1+, underwent mastectomy" | Treatment == "lef1+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1+, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "lef1+, underwent mastectomy" | Treatment == "lef1+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1+, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: lef1 + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "lef1-, underwent mastectomy" | Treatment == "lef1-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1-, underwent mastectomy", "mastectomy", "breast conservation") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "lef1-, underwent mastectomy" | Treatment == "lef1-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "lef1-, underwent mastectomy", "mastectomy", "breast conservation") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: lef1 - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_lef1_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
#prepare long format dataset
long_ic2dat <- gen_stan_dat(ic2surv)
m3.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "hormone_therapy", "kras:hormone_therapy") )
m4.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "radio_therapy", "kras:radio_therapy") )
m5.map2stan <- post_surv(x = ic2surv, surv_form = c("kras", "chemotherapy", "kras:chemotherapy") )
summary(m5.map2stan)
summary(m5.map2stan)
#Conterfactual plot
timepoints = seq(min(ic2surv$time), max(ic2surv$time), length.out = 100)
dtime = c(0, diff(timepoints))
bcdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = F,
kras = F
)
bchdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = F,
kras = T
)
masdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = T,
kras = F
)
mashdata <- data.frame(
time = timepoints,
log_dtime = log(dtime),
chemotherapy = T,
kras = T
)
bc.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = bcdata, treatment = "kras-, underwent breast conservation")
bch.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = bchdata, treatment = "kras+, underwent breast conservation")
mas.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = masdata, treatment = "kras-, underwent chemotherapy")
mash.plot.frame <- get_plot_new_frame(mod = m5.map2stan, newdat = mashdata, treatment = "kras+, underwent chemotherapy")
data.plot <- rbind(bc.plot.frame, mas.plot.frame, bch.plot.frame, mash.plot.frame)
obs.mortality <- get_km.frame(ic2surv, strata = c("kras", "chemotherapy") )
obs.mortality$kras <- as.logical(obs.mortality$kras)
obs.mortality$chemotherapy <- as.logical(obs.mortality$chemotherapy)
obs.mortality <- obs.mortality %>%
dplyr::mutate(Treatment = ifelse(kras & chemotherapy, "kras+, underwent chemotherapy", ifelse( !(kras) & (chemotherapy), "kras-, underwent chemotherapy", ifelse( kras & !chemotherapy, "kras+, underwent breast conservation", "kras-, underwent breast conservation"))))
pp <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras+, underwent chemotherapy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent chemotherapy", "chemotherapy", "no chemotherapy") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras+, underwent chemotherapy" | Treatment == "kras+, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras+, underwent chemotherapy", "chemotherapy", "no chemotherapy") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras + by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
pn <- ggplot2::ggplot(data.plot %>%
filter(Treatment == "kras-, underwent chemotherapy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent chemotherapy", "chemotherapy", "no chemotherapy") ),
aes(time, survmean,
group = Treatment, colour = Treatment)) +
geom_line(mapping=aes(group = Treatment, colour = Treatment), alpha = 0.8) +
geom_ribbon(aes( ymin = survlower,
ymax = survupper,
fill = Treatment), alpha = 0.5, colour=NA) +
geom_step(data= obs.mortality %>%
filter(Treatment == "kras-, underwent chemotherapy" | Treatment == "kras-, underwent breast conservation") %>%
mutate(Treatment = ifelse(Treatment == "kras-, underwent chemotherapy", "chemotherapy", "no chemotherapy") ), aes(time, surv, colour = Treatment), alpha = 0.9)+
labs(title = "iC-2: kras - by type of surgery",
subtitle = "Survival counterfactual plot",
caption = "4400 posterior sample size") +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Survival probability") +
xlab("Time (months)") +
theme_bw() + theme(legend.position=c(0.7, 0.92))
require(gridExtra)
grid.arrange(pp, pn, ncol = 2)
pdf('counterfactual_kras_surgery.pdf')
grid.arrange(pp, pn, ncol = 2)
dev.off()
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