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simscript.R
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1346 lines (1152 loc) · 71.9 KB
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# devtools::install_github("pavlakrotka/NCC@v1.4", force = TRUE)
library(NCC)
library(tidyverse)
n_sim <- 10000
##############################################################################################################################################################
# FUNCTION BY DOMINIC
## given expected and 'maximum' jump in the mean between periods/buckets,
## what are the corresponding tau_a and tau_b?
from_jump_size_to_tau_a_b <- function(e_jump_size = 0.01, max_jump_size = 0.1){
fun_to_solve <- function(tau_b, max_jump_size, e_jump_size){
pgamma(1 / max_jump_size ^ 2, 1 / (e_jump_size ^ 2) * tau_b, tau_b) - 0.01
# pgamma(1 / max_jump_size ^ 2, 1 / (e_jump_size ^ 2) * tau_b, 1/tau_b) - 0.01
}
tau_b <- uniroot(fun_to_solve,
c(1e-12, 1e12),
max_jump_size = max_jump_size,
e_jump_size = e_jump_size)$root
tau_a <- 1 / e_jump_size ^ 2 * tau_b
c(tau_a = tau_a,
tau_b = tau_b)
}
##############################################################################################################################################################
# SCENARIO I ----
# 3 arms
# equal and different time trends
# vary d and lambda
# N_peak for different d's (always middle of the trial):
# sum(get_ss_matrix(num_arms = 3, n_arm = 250, d = 0*(0:2)), na.rm=T)/2
# sum(get_ss_matrix(num_arms = 3, n_arm = 250, d = 125*(0:2)), na.rm=T)/2
# sum(get_ss_matrix(num_arms = 3, n_arm = 250, d = 250*(0:2)), na.rm=T)/2
# sum(get_ss_matrix(num_arms = 3, n_arm = 250, d = 375*(0:2)), na.rm=T)/2
# sum(get_ss_matrix(num_arms = 3, n_arm = 250, d = 500*(0:2)), na.rm=T)/2
## EQUAL TIME TREND ----
### TYPE I ERROR ----
set.seed(1)
scenario_i_eq_alpha <- data.frame(num_arms = 3,
n_arm = 250,
d1 = seq(0, 500, length.out=5)*0,
d2 = seq(0, 500, length.out=5)*1,
d3 = seq(0, 500, length.out=5)*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 5),
N_peak = c(rep(NA, 10), c(500, 553, 605, 672, 750)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_i_eq_alpha <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_alpha, arms = c(2:3), models = c("fixmodel", "sepmodel", "poolmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_alpha, "results/results_i_eq_alpha.csv")
### TIME MACHINE CALIBRATION ----
set.seed(2)
scenario_i_eq_alpha_TM <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = seq(-0.15, 0.15, length.out = 9),
lambda2 = seq(-0.15, 0.15, length.out = 9),
lambda3 = seq(-0.15, 0.15, length.out = 9),
trend = "stepwise_2",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = rep(c(from_jump_size_to_tau_a_b(e_jump_size = 10, max_jump_size = 15)[1],
from_jump_size_to_tau_a_b(e_jump_size = 5, max_jump_size = 10)[1],
from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1],
from_jump_size_to_tau_a_b(e_jump_size = 0.01, max_jump_size = 0.15)[1],
from_jump_size_to_tau_a_b(e_jump_size = 0.001, max_jump_size = 0.015)[1]), each=9),
tau_b = rep(c(from_jump_size_to_tau_a_b(e_jump_size = 10, max_jump_size = 15)[2],
from_jump_size_to_tau_a_b(e_jump_size = 5, max_jump_size = 10)[2],
from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2],
from_jump_size_to_tau_a_b(e_jump_size = 0.01, max_jump_size = 0.15)[2],
from_jump_size_to_tau_a_b(e_jump_size = 0.001, max_jump_size = 0.015)[2]), each=9),
tau_case = rep(c("e-10, m-15", "e-5, m-10", "e-1, m-1.5", "e-0.01, m-0.15", "e-0.001, m-0.015"), each=9),
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = NA,
prior_prec_tau = NA,
prior_prec_eta = NA,
n_samples = NA,
n_chains = NA,
n_iter = NA,
n_adapt = NA,
robustify = NA,
weight = NA)
results_i_eq_alpha_TM <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_alpha_TM, arms = c(3), models = c("timemachine", "fixmodel"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_alpha_TM, "results/results_i_eq_alpha_TM.csv")
### MAP PRIOR CALIBRATION ----
set.seed(3)
scenario_i_eq_alpha_MAP_weight_allpriors <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 375*0,
d2 = 375*1,
d3 = 375*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = seq(-0.15, 0.15, length.out = 9),
lambda2 = seq(-0.15, 0.15, length.out = 9),
lambda3 = seq(-0.15, 0.15, length.out = 9),
trend = "stepwise_2",
N_peak = NA,
alpha = 0.025,
prec_theta = NA,
prec_eta = NA,
tau_a = NA,
tau_b = NA,
tau_case = NA,
prec_a = NA,
prec_b = NA,
bucket_size = NA,
opt = 2,
prior_prec_tau = rep(c(2, 0.2, 0.002), each=9),
prior_prec_eta = c(rep(0.001, 9*3), rep(1, 9*3)),
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = rep(c(0.1, 0.2, 0.5), each = 9*3*2))
results_i_eq_alpha_MAP_weight_allpriors <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_alpha_MAP_weight_allpriors, arms = c(3), models = c("MAPpriorNew", "fixmodel"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_alpha_MAP_weight_allpriors, "results/results_i_eq_alpha_MAP_weight_allpriors.csv")
### POWER ----
set.seed(4)
scenario_i_eq_pow <- data.frame(num_arms = 3,
n_arm = 250,
d1 = seq(0, 500, length.out=5)*0,
d2 = seq(0, 500, length.out=5)*1,
d3 = seq(0, 500, length.out=5)*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 5),
N_peak = c(rep(NA, 10), c(500, 553, 605, 672, 750)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_i_eq_pow <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_pow, arms = c(2:3), models = c("fixmodel", "sepmodel", "poolmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_pow, "results/results_i_eq_pow.csv")
set.seed(2)
scenario_i_eq_pow_TM <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = seq(-0.15, 0.15, length.out = 9),
lambda2 = seq(-0.15, 0.15, length.out = 9),
lambda3 = seq(-0.15, 0.15, length.out = 9),
trend = "stepwise_2",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = rep(c(from_jump_size_to_tau_a_b(e_jump_size = 10, max_jump_size = 15)[1],
from_jump_size_to_tau_a_b(e_jump_size = 5, max_jump_size = 10)[1],
from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1],
from_jump_size_to_tau_a_b(e_jump_size = 0.01, max_jump_size = 0.15)[1],
from_jump_size_to_tau_a_b(e_jump_size = 0.001, max_jump_size = 0.015)[1]), each=9),
tau_b = rep(c(from_jump_size_to_tau_a_b(e_jump_size = 10, max_jump_size = 15)[2],
from_jump_size_to_tau_a_b(e_jump_size = 5, max_jump_size = 10)[2],
from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2],
from_jump_size_to_tau_a_b(e_jump_size = 0.01, max_jump_size = 0.15)[2],
from_jump_size_to_tau_a_b(e_jump_size = 0.001, max_jump_size = 0.015)[2]), each=9),
tau_case = rep(c("e-10, m-15", "e-5, m-10", "e-1, m-1.5", "e-0.01, m-0.15", "e-0.001, m-0.015"), each=9),
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = NA,
prior_prec_tau = NA,
prior_prec_eta = NA,
n_samples = NA,
n_chains = NA,
n_iter = NA,
n_adapt = NA,
robustify = NA,
weight = NA)
results_i_eq_pow_TM <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_pow_TM, arms = c(3), models = c("timemachine", "fixmodel"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_pow_TM, "results/results_i_eq_pow_TM.csv")
set.seed(3)
scenario_i_eq_pow_MAP_weight_allpriors <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 375*0,
d2 = 375*1,
d3 = 375*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = seq(-0.15, 0.15, length.out = 9),
lambda2 = seq(-0.15, 0.15, length.out = 9),
lambda3 = seq(-0.15, 0.15, length.out = 9),
trend = "stepwise_2",
N_peak = NA,
alpha = 0.025,
prec_theta = NA,
prec_eta = NA,
tau_a = NA,
tau_b = NA,
tau_case = NA,
prec_a = NA,
prec_b = NA,
bucket_size = NA,
opt = 2,
prior_prec_tau = rep(c(2, 0.2, 0.002), each=9),
prior_prec_eta = c(rep(0.001, 9*3), rep(1, 9*3)),
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = rep(c(0.1, 0.2, 0.5), each = 9*3*2))
results_i_eq_pow_MAP_weight_allpriors <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_pow_MAP_weight_allpriors, arms = c(3), models = c("MAPpriorNew", "fixmodel"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_pow_MAP_weight_allpriors, "results/results_i_eq_pow_MAP_weight_allpriors.csv")
## DIFFERENT TREND IN ARM 1 ----
### TYPE I ERROR ----
set.seed(5)
scenario_i_diff1_alpha <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.1,
lambda1 = seq(-0.05, 0.25, length.out = 9),
lambda2 = 0.1,
lambda3 = 0.1,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 9),
N_peak = c(rep(NA, 18), rep(605, 9)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_i_diff1_alpha <- sim_study_par(nsim = n_sim, scenarios = scenario_i_diff1_alpha, arms = c(3), models = c("fixmodel", "sepmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_diff1_alpha, "results/results_i_diff1_alpha.csv")
## DIFFERENT TREND IN ARMS 1 & 2 ----
### TYPE I ERROR ----
set.seed(6)
scenario_i_diff12_alpha <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.1,
lambda1 = seq(-0.05, 0.25, length.out = 9),
lambda2 = seq(-0.05, 0.25, length.out = 9),
lambda3 = 0.1,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 9),
N_peak = c(rep(NA, 18), rep(605, 9)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_i_diff12_alpha <- sim_study_par(nsim = n_sim, scenarios = scenario_i_diff12_alpha, arms = c(3), models = c("fixmodel", "sepmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_diff12_alpha, "results/results_i_diff12_alpha.csv")
##############################################################################################################################################################
# SCENARIO II ----
# 4 arms with non-equidistant entry time
# varying d3
# only equal time trend
# N_peak for different d's (always middle of the trial):
# sum(get_ss_matrix(num_arms = 4, n_arm = 250, d = c(0, 300, 300, 800)), na.rm = T)/2
# sum(get_ss_matrix(num_arms = 4, n_arm = 250, d = c(0, 300, 425, 800)), na.rm = T)/2
# sum(get_ss_matrix(num_arms = 4, n_arm = 250, d = c(0, 300, 550, 800)), na.rm = T)/2
# sum(get_ss_matrix(num_arms = 4, n_arm = 250, d = c(0, 300, 675, 800)), na.rm = T)/2
# sum(get_ss_matrix(num_arms = 4, n_arm = 250, d = c(0, 300, 800, 800)), na.rm = T)/2
## TYPE I ERROR ----
set.seed(7)
scenario_ii_eq_alpha <- data.frame(num_arms = 4,
n_arm = 250,
d1 = 0,
d2 = 300,
d3 = seq(300, 800, length.out=5),
d4 = 800,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
theta4 = 0,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
lambda4 = 0.15,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 5),
N_peak = c(rep(NA, 10), c(767, 774, 781, 790, 800)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_ii_eq_alpha <- sim_study_par(nsim = n_sim, scenarios = scenario_ii_eq_alpha, arms = c(4), models = c("fixmodel", "sepmodel", "poolmodel", "MAPpriorNew", "timemachine"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_ii_eq_alpha, "results/results_ii_eq_alpha.csv")
## POWER ----
# power.t.test(n=250, sd=1, sig.level = 0.025, power=0.8, type = "two.sample", alternative = "one.sided")
set.seed(8)
scenario_ii_eq_pow <- data.frame(num_arms = 4,
n_arm = 250,
d1 = 0,
d2 = 300,
d3 = seq(300, 800, length.out=5),
d4 = 800,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
theta4 = 0.25,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
lambda4 = 0.15,
trend = rep(c("linear", "stepwise_2", "inv_u"), each = 5),
N_peak = c(rep(NA, 10), c(767, 774, 781, 790, 800)),
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_ii_eq_pow <- sim_study_par(nsim = n_sim, scenarios = scenario_ii_eq_pow, arms = c(4), models = c("fixmodel", "sepmodel", "poolmodel", "MAPpriorNew", "timemachine"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_ii_eq_pow, "results/results_ii_eq_pow.csv")
##############################################################################################################################################################
# SCENARIO III-II ----
# 10 arms
# random time trend in arms 1-9
# vary time trends in arms 0 and 10
# inference on arm 10
## TYPE I ERROR ----
set.seed(11)
scenario_iii_ii_rand_alpha <- data.frame(num_arms = 10,
n_arm = 250,
d1 = 300*0,
d2 = 300*1,
d3 = 300*2,
d4 = 300*3,
d5 = 300*4,
d6 = 300*5,
d7 = 300*6,
d8 = 300*7,
d9 = 300*8,
d10 = 300*9,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
theta4 = 0,
theta5 = 0,
theta6 = 0,
theta7 = 0,
theta8 = 0,
theta9 = 0,
theta10 = 0,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = "random",
lambda2 = "random",
lambda3 = "random",
lambda4 = "random",
lambda5 = "random",
lambda6 = "random",
lambda7 = "random",
lambda8 = "random",
lambda9 = "random",
lambda10 = seq(-0.15, 0.15, length.out = 9),
trend = "linear",
N_peak = NA,
trend_mean = seq(-0.15, 0.15, length.out = 9),
trend_var = 0.5,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_iii_ii_rand_alpha <- sim_study_par(nsim = n_sim, scenarios = scenario_iii_ii_rand_alpha, arms = c(10), models = c("fixmodel", "sepmodel", "poolmodel", "MAPpriorNew", "timemachine"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_iii_ii_rand_alpha, "results/results_iii_ii_rand_alpha.csv")
## POWER ----
# power.t.test(n=250, sd=1, sig.level = 0.025, power=0.8, type = "two.sample", alternative = "one.sided")
set.seed(12)
scenario_iii_ii_rand_pow <- data.frame(num_arms = 10,
n_arm = 250,
d1 = 300*0,
d2 = 300*1,
d3 = 300*2,
d4 = 300*3,
d5 = 300*4,
d6 = 300*5,
d7 = 300*6,
d8 = 300*7,
d9 = 300*8,
d10 = 300*9,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
theta4 = 0.25,
theta5 = 0.25,
theta6 = 0.25,
theta7 = 0.25,
theta8 = 0.25,
theta9 = 0.25,
theta10 = 0.25,
lambda0 = seq(-0.15, 0.15, length.out = 9),
lambda1 = "random",
lambda2 = "random",
lambda3 = "random",
lambda4 = "random",
lambda5 = "random",
lambda6 = "random",
lambda7 = "random",
lambda8 = "random",
lambda9 = "random",
lambda10 = seq(-0.15, 0.15, length.out = 9),
trend = "linear",
N_peak = NA,
trend_mean = seq(-0.15, 0.15, length.out = 9),
trend_var = 0.5,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1)
results_iii_ii_rand_pow <- sim_study_par(nsim = n_sim, scenarios = scenario_iii_ii_rand_pow, arms = c(10), models = c("fixmodel", "sepmodel", "poolmodel", "MAPpriorNew", "timemachine"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_iii_ii_rand_pow, "results/results_iii_ii_rand_pow.csv")
##################################################################################################################################################################################################################################
# ADDED: SEASONAL TIME TREND ----
# SCENARIO I ----
# 3 arms
# equal and different time trends
# vary d and lambda
## EQUAL TIME TREND ----
### TYPE I ERROR ----
set.seed(1)
scenario_i_eq_alpha_seasonal <- data.frame(num_arms = 3,
n_arm = 250,
d1 = seq(0, 500, length.out=5)*0,
d2 = seq(0, 500, length.out=5)*1,
d3 = seq(0, 500, length.out=5)*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1,
n_wave = 2)
results_i_eq_alpha_seasonal <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_alpha_seasonal, arms = c(2:3), models = c("fixmodel", "sepmodel", "poolmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_alpha_seasonal, "results/results_i_eq_alpha_seasonal.csv")
### POWER ----
set.seed(4)
scenario_i_eq_pow_seasonal <- data.frame(num_arms = 3,
n_arm = 250,
d1 = seq(0, 500, length.out=5)*0,
d2 = seq(0, 500, length.out=5)*1,
d3 = seq(0, 500, length.out=5)*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1,
n_wave = 2)
results_i_eq_pow_seasonal <- sim_study_par(nsim = n_sim, scenarios = scenario_i_eq_pow_seasonal, arms = c(2:3), models = c("fixmodel", "sepmodel", "poolmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_eq_pow_seasonal, "results/results_i_eq_pow_seasonal.csv")
## DIFFERENT TREND IN ARM 1 ----
### TYPE I ERROR ----
set.seed(5)
scenario_i_diff1_alpha_seasonal <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.1,
lambda1 = seq(-0.05, 0.25, length.out = 9),
lambda2 = 0.1,
lambda3 = 0.1,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1,
n_wave = 2)
results_i_diff1_alpha_seasonal <- sim_study_par(nsim = n_sim, scenarios = scenario_i_diff1_alpha_seasonal, arms = c(3), models = c("fixmodel", "sepmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_diff1_alpha_seasonal, "results/results_i_diff1_alpha_seasonal.csv")
## DIFFERENT TREND IN ARMS 1 & 2 ----
### TYPE I ERROR ----
set.seed(6)
scenario_i_diff12_alpha_seasonal <- data.frame(num_arms = 3,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
lambda0 = 0.1,
lambda1 = seq(-0.05, 0.25, length.out = 9),
lambda2 = seq(-0.05, 0.25, length.out = 9),
lambda3 = 0.1,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1,
n_wave = 2)
results_i_diff12_alpha_seasonal <- sim_study_par(nsim = n_sim, scenarios = scenario_i_diff12_alpha_seasonal, arms = c(3), models = c("fixmodel", "sepmodel", "timemachine", "MAPpriorNew"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_i_diff12_alpha_seasonal, "results/results_i_diff12_alpha_seasonal.csv")
##############################################################################################################################################################
# SCENARIO II ----
# 4 arms with non-equidistant entry time
# varying d3
# only equal time trend
## TYPE I ERROR ----
set.seed(7)
scenario_ii_eq_alpha_seasonal <- data.frame(num_arms = 4,
n_arm = 250,
d1 = 0,
d2 = 300,
d3 = seq(300, 800, length.out=5),
d4 = 800,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
theta4 = 0,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
lambda4 = 0.15,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,
robustify = TRUE,
weight = 0.1,
n_wave = 2)
results_ii_eq_alpha_seasonal <- sim_study_par(nsim = n_sim, scenarios = scenario_ii_eq_alpha_seasonal, arms = c(4), models = c("fixmodel", "sepmodel", "poolmodel", "MAPpriorNew", "timemachine"), endpoint = "cont", perc_cores = 0.99)
write_csv(results_ii_eq_alpha_seasonal, "results/results_ii_eq_alpha_seasonal.csv")
## POWER ----
# power.t.test(n=250, sd=1, sig.level = 0.025, power=0.8, type = "two.sample", alternative = "one.sided")
set.seed(8)
scenario_ii_eq_pow_seasonal <- data.frame(num_arms = 4,
n_arm = 250,
d1 = 0,
d2 = 300,
d3 = seq(300, 800, length.out=5),
d4 = 800,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0.25,
theta2 = 0.25,
theta3 = 0.25,
theta4 = 0.25,
lambda0 = 0.15,
lambda1 = 0.15,
lambda2 = 0.15,
lambda3 = 0.15,
lambda4 = 0.15,
trend = "seasonal",
N_peak = NA,
alpha = 0.025,
prec_theta = 0.001,
prec_eta = 0.001,
tau_a = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[1]),
tau_b = unname(from_jump_size_to_tau_a_b(e_jump_size = 1, max_jump_size = 1.5)[2]),
tau_case = "reasonable",
prec_a = 0.001,
prec_b = 0.001,
bucket_size = 25,
opt = 2,
prior_prec_tau = 0.002,
prior_prec_eta = 0.001,
n_samples = 1000,
n_chains = 4,
n_iter = 4000,
n_adapt = 1000,