Nothing
inst/doc/CITIES_demo.R
In cities: Clinical Trials with Intercurrent Events Simulator
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
## -----------------------------------------------------------------------------
library(cities)
library(dplyr)
library(tidyr)
library(plotly)
library(ggplot2)
library(ggthemes)
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 50
reference_id = 1
threshold = NA
timepoints = c(0,24,48,72,96,120,144)
IR_display = TRUE
delta_adjustment_in = c(0,1)
n_patient_ctrl = 120
n_patient_expt = 150
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
mean_control = c(0,0,0,0,0,0,0)
mean_treatment = c(0,0.1,0.2,0.4,0.6,0.8,1)
mean_list = list(mean_control, mean_treatment)
sigma_ar_vec = c(1, 1)
pacf_list = list(c(-0.2, 0.4),
c(-0.2, 0.4))
beta_list = list(c(1.25, 1.25),
c(1.25, 1.25))
covariate_df = NA
# LoE & EE
up_good = "Up"
p_loe_max = 0.75
z_l_loe = -7
z_u_loe = -1
p_ee_max = 0.1
z_l_ee = 4
z_u_ee = 10
# Admin & AE
p_admin_ctrl = 0.02
p_admin_expt = 0.02
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.7
prob_ae_expt = 0.9
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.1
rate_dc_ae_expt = 0.1
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', out.width="50%", fig.keep='all'---------------
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', out.width="50%", fig.keep='all'---------------
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
reference_id = reference_id,
IR_display = IR_display,
normal_output = TRUE,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4,6,7)]
## -----------------------------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 100
reference_id = 1
threshold = NA
timepoints = c(0,6,12,18,26)
IR_display = FALSE
delta_adjustment_in = NA
n_patient_ctrl = 195
n_patient_expt = 192
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
mean_control = c(8, 8, 7.98, 7.97, 7.94)
mean_treatment = c(8, 7.45, 7.26, 7.21, 7.16)
mean_list = list(mean_control, mean_treatment)
sigma_ar_vec = c(0.8, 0.8)
pacf_list = c(0.5, 0.5)
beta_list = NA
covariate_df = NA
# LoE & EE
up_good = "Down"
p_loe_max = 0.25
z_l_loe = 1
z_u_loe = 4
p_ee_max = 0
z_l_ee = 0
z_u_ee = 0
# Admin & AE
p_admin_ctrl = 0.03 #(2+3+1+15+1+4)/192 = 0.1354167
p_admin_expt = 0.02 #(5+2+9+3)/197 = 0.0964467
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.53 #101/192
prob_ae_expt = 0.6 #118/197
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.01 # 2/192
rate_dc_ae_expt = 0.02 # 4/197
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
reference_id = reference_id,
IR_display = IR_display,
normal_output = TRUE,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4)]
## -----------------------------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 100
reference_id = 1
threshold = NA
timepoints = c(0, 12, 24, 36, 52, 64, 76)
IR_display = FALSE
delta_adjustment_in = NA
n_patient_ctrl = 126
n_patient_expt = 131
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
sigma_ar_vec = c(10, 10)
pacf_list = c(0.5, 0.5)
mean_control = c(106, 105.87, 104.81, 102.85, 99.31, 97.67, 95.97)
mean_treatment = c(106, 106.35, 106.06, 105.29, 102.98, 101.09, 99.17)
mean_list = list(mean_control, mean_treatment)
beta_list = NA
covariate_df = NA
# LoE & EE
up_good = "Up"
p_loe_max = 0
z_l_loe = 0
z_u_loe = 0
p_ee_max = 0
z_l_ee = 0
z_u_ee = 0
# Admin & AE
p_admin_ctrl = 0.03#
p_admin_expt = 0.01 #
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.9 #
prob_ae_expt = 0.9 #
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.07 #
rate_dc_ae_expt = 0.3 #
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%",message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%",message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
IR_display = IR_display,
reference_id = reference_id,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4)]
## ---- out.width="50%"---------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
p_admin_expt = 0.005
p_admin_ctrl = 0.005
prob_ae_expt = 0.6
rate_dc_ae_expt = 0.1
prob_ae_ctrl = 0.3
rate_dc_ae_ctrl = 0.1
n_patient_expt = 120
n_patient_expt2 = 100
n_patient_ctrl = 150
mean_treatment = c(0, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5)
mean_treatment2 = 2*c(0, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5)
mean_control = rep(0, length(mean_treatment))
beta_control = c(1.25, 1.25, 1)
beta_expt = c(1.25, 1.25, 1)
beta_expt2 = c(1.25, 1.25, 1)
p_loe_max = 0.3
z_l_loe = -5
z_u_loe = -3
p_ee_max = 0.1
z_l_ee = 6
z_u_ee = 7.5
timepoints = c(0:(length(mean_treatment)-1))*24
up_good = "Up"
delta_adjustment_in = NA
threshold = NA
mean_list = list(mean_control, mean_treatment, mean_treatment2)
p_admin = c(p_admin_ctrl, p_admin_expt, p_admin_expt)
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt, rate_dc_ae_expt)
prob_ae = c(prob_ae_ctrl, prob_ae_expt,prob_ae_expt)
n_patient_vector = c(n_patient_ctrl, n_patient_expt, n_patient_expt2)
sigma_ar_vec = NA
total_data = 20
reference_id = 1
starting_seed_val = 1
A = matrix(runif(length(timepoints)^2)*2-1, ncol=length(timepoints))
Sigma = t(A) %*% A
pacf_list = list(Sigma,
Sigma,
Sigma)
n_total = sum(n_patient_vector)
beta_list = list(beta_control, beta_expt, beta_expt2)
covariate_df = data.frame(continuous_1 = (rnorm(n = n_total, mean = 0, sd = 1)),
continuous_2 = (rnorm(n = n_total, mean = 0, sd = 1)),
binary_1 = rbinom(n = n_total, size = 1, prob = 0.5))
starting_seed_val = 1
static_output = TRUE
IR_display = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
IR_display = IR_display,
reference_id = reference_id,
static_output = static_output)
## -----------------------------------------------------------------------------
estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
## ---- out.width="50%"---------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
Try the cities package in your browser
Any scripts or data that you put into this service are public.
cities documentation built on Aug. 8, 2023, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
## -----------------------------------------------------------------------------
library(cities)
library(dplyr)
library(tidyr)
library(plotly)
library(ggplot2)
library(ggthemes)
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 50
reference_id = 1
threshold = NA
timepoints = c(0,24,48,72,96,120,144)
IR_display = TRUE
delta_adjustment_in = c(0,1)
n_patient_ctrl = 120
n_patient_expt = 150
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
mean_control = c(0,0,0,0,0,0,0)
mean_treatment = c(0,0.1,0.2,0.4,0.6,0.8,1)
mean_list = list(mean_control, mean_treatment)
sigma_ar_vec = c(1, 1)
pacf_list = list(c(-0.2, 0.4),
c(-0.2, 0.4))
beta_list = list(c(1.25, 1.25),
c(1.25, 1.25))
covariate_df = NA
# LoE & EE
up_good = "Up"
p_loe_max = 0.75
z_l_loe = -7
z_u_loe = -1
p_ee_max = 0.1
z_l_ee = 4
z_u_ee = 10
# Admin & AE
p_admin_ctrl = 0.02
p_admin_expt = 0.02
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.7
prob_ae_expt = 0.9
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.1
rate_dc_ae_expt = 0.1
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', out.width="50%", fig.keep='all'---------------
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', out.width="50%", fig.keep='all'---------------
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
reference_id = reference_id,
IR_display = IR_display,
normal_output = TRUE,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4,6,7)]
## -----------------------------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 100
reference_id = 1
threshold = NA
timepoints = c(0,6,12,18,26)
IR_display = FALSE
delta_adjustment_in = NA
n_patient_ctrl = 195
n_patient_expt = 192
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
mean_control = c(8, 8, 7.98, 7.97, 7.94)
mean_treatment = c(8, 7.45, 7.26, 7.21, 7.16)
mean_list = list(mean_control, mean_treatment)
sigma_ar_vec = c(0.8, 0.8)
pacf_list = c(0.5, 0.5)
beta_list = NA
covariate_df = NA
# LoE & EE
up_good = "Down"
p_loe_max = 0.25
z_l_loe = 1
z_u_loe = 4
p_ee_max = 0
z_l_ee = 0
z_u_ee = 0
# Admin & AE
p_admin_ctrl = 0.03 #(2+3+1+15+1+4)/192 = 0.1354167
p_admin_expt = 0.02 #(5+2+9+3)/197 = 0.0964467
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.53 #101/192
prob_ae_expt = 0.6 #118/197
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.01 # 2/192
rate_dc_ae_expt = 0.02 # 4/197
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
reference_id = reference_id,
IR_display = IR_display,
normal_output = TRUE,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4)]
## -----------------------------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
total_data = 100
reference_id = 1
threshold = NA
timepoints = c(0, 12, 24, 36, 52, 64, 76)
IR_display = FALSE
delta_adjustment_in = NA
n_patient_ctrl = 126
n_patient_expt = 131
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_total = sum(n_patient_vector)
sigma_ar_vec = c(10, 10)
pacf_list = c(0.5, 0.5)
mean_control = c(106, 105.87, 104.81, 102.85, 99.31, 97.67, 95.97)
mean_treatment = c(106, 106.35, 106.06, 105.29, 102.98, 101.09, 99.17)
mean_list = list(mean_control, mean_treatment)
beta_list = NA
covariate_df = NA
# LoE & EE
up_good = "Up"
p_loe_max = 0
z_l_loe = 0
z_u_loe = 0
p_ee_max = 0
z_l_ee = 0
z_u_ee = 0
# Admin & AE
p_admin_ctrl = 0.03#
p_admin_expt = 0.01 #
p_admin = c(p_admin_ctrl, p_admin_expt)
prob_ae_ctrl = 0.9 #
prob_ae_expt = 0.9 #
prob_ae = c(prob_ae_ctrl, prob_ae_expt)
rate_dc_ae_ctrl = 0.07 #
rate_dc_ae_expt = 0.3 #
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt)
starting_seed_val = 1
static_output = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%",message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all',out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%",message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
IR_display = IR_display,
reference_id = reference_id,
static_output = static_output)
## -----------------------------------------------------------------------------
display_table = estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -Arm, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
display_table[,c(1,2,5,3,4)]
## ---- out.width="50%"---------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
## ----echo = T, results = 'hide'-----------------------------------------------
p_admin_expt = 0.005
p_admin_ctrl = 0.005
prob_ae_expt = 0.6
rate_dc_ae_expt = 0.1
prob_ae_ctrl = 0.3
rate_dc_ae_ctrl = 0.1
n_patient_expt = 120
n_patient_expt2 = 100
n_patient_ctrl = 150
mean_treatment = c(0, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5)
mean_treatment2 = 2*c(0, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5)
mean_control = rep(0, length(mean_treatment))
beta_control = c(1.25, 1.25, 1)
beta_expt = c(1.25, 1.25, 1)
beta_expt2 = c(1.25, 1.25, 1)
p_loe_max = 0.3
z_l_loe = -5
z_u_loe = -3
p_ee_max = 0.1
z_l_ee = 6
z_u_ee = 7.5
timepoints = c(0:(length(mean_treatment)-1))*24
up_good = "Up"
delta_adjustment_in = NA
threshold = NA
mean_list = list(mean_control, mean_treatment, mean_treatment2)
p_admin = c(p_admin_ctrl, p_admin_expt, p_admin_expt)
rate_dc_ae = c(rate_dc_ae_ctrl, rate_dc_ae_expt, rate_dc_ae_expt)
prob_ae = c(prob_ae_ctrl, prob_ae_expt,prob_ae_expt)
n_patient_vector = c(n_patient_ctrl, n_patient_expt, n_patient_expt2)
sigma_ar_vec = NA
total_data = 20
reference_id = 1
starting_seed_val = 1
A = matrix(runif(length(timepoints)^2)*2-1, ncol=length(timepoints))
Sigma = t(A) %*% A
pacf_list = list(Sigma,
Sigma,
Sigma)
n_total = sum(n_patient_vector)
beta_list = list(beta_control, beta_expt, beta_expt2)
covariate_df = data.frame(continuous_1 = (rnorm(n = n_total, mean = 0, sd = 1)),
continuous_2 = (rnorm(n = n_total, mean = 0, sd = 1)),
binary_1 = rbinom(n = n_total, size = 1, prob = 0.5))
starting_seed_val = 1
static_output = TRUE
IR_display = TRUE
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
mean_out = plot_means(n_patient_vector = n_patient_vector,
timepoints = timepoints,
pacf_list = pacf_list,
sigma_ar_vec = sigma_ar_vec,
mean_list = mean_list,
beta_list = beta_list,
reference_id = reference_id,
seed_val = starting_seed_val,
total_data = total_data,
threshold = threshold,
covariate_df = covariate_df,
static_output = static_output)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
plot_loe_ee (mean_list = mean_list,
ref_grp = reference_id,
stdev_vec = sigma_ar_vec,
p_loe_max = p_loe_max,
z_l_loe = z_l_loe,
z_u_loe = z_u_loe,
p_ee_max = p_ee_max,
z_l_ee = z_l_ee,
z_u_ee = z_u_ee,
up_good = up_good,
greyscale = FALSE,
static_output = TRUE)
## ----echo=TRUE, results='hide', fig.keep='all', out.width="50%", message = FALSE----
data_out = data_generator_loop(n_patient_vector,
p_loe_max,
z_l_loe,
z_u_loe,
p_ee_max,
z_l_ee,
z_u_ee,
timepoints,
pacf_list,
sigma_ar_vec,
mean_list,
beta_list,
p_admin,
rate_dc_ae,
prob_ae,
starting_seed_val,
reference_id,
plot_po = FALSE,
up_good,
threshold,
total_data,
delta_adjustment_in,
covariate_df)
estimates_out = plot_estimates(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
IR_display = IR_display,
reference_id = reference_id,
static_output = static_output)
## -----------------------------------------------------------------------------
estimates_out %>%
mutate(mean_se = paste0(mean, " (", round(se, 2) , ")")) %>%
dplyr::select(-se, -mean) %>%
pivot_wider(names_from = Estimand, values_from = mean_se)
## ---- out.width="50%"---------------------------------------------------------
dc_out = plot_dc(data_out = data_out,
total_data = total_data,
timepoints = timepoints,
static_output = static_output)
## -----------------------------------------------------------------------------
dc_out1 = dc_out %>%
ungroup() %>%
filter(Timepoints == max(timepoints),
Reason != "OVERALL") %>%
select(Arm, Reason, Value) %>%
pivot_wider(names_from = Arm,
values_from = Value) %>%
arrange(factor(Reason, levels = c("AE", "LOE", "EE", "ADMIN")))
rbind(dc_out1,c("OVERALL",colSums(dc_out1[,-1])))
Try the cities package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.