paper/sect4.2_example.R

In pavlakrotka/NCC: Simulation and Analysis of Platform Trials with Non-Concurrent Controls

##################################################################################
# Code for the paper
# "NCC: An R-package for analysis and simulation of platform trials with non-concurrent controls" by Pavla Krotka et al. (2023)
# Example 2 - Section 4
##################################################################################

library(NCC)
library(ggplot2)

# lambda_values <- rep(seq(-0.15, 0.15, length.out = 9), 2)
# sim_scenarios <- data.frame(num_arms = 4,
#                             n_arm = 250,
#                             d1 = 250*0,
#                             d2 = 250*1,
#                             d3 = 250*2,
#                             d4 = 250*3,
#                             period_blocks = 2,
#                             mu0 = 0,
#                             sigma = 1,
#                             theta1 = 0,
#                             theta2 = 0,
#                             theta3 = 0,
#                             theta4 = 0,
#                             lambda0 = lambda_values,
#                             lambda1 = lambda_values,
#                             lambda2 = lambda_values,
#                             lambda3 = lambda_values,
#                             lambda4 = lambda_values,
#                             trend = c(rep("linear", 9), rep("stepwise_2", 9)),
#                             alpha = 0.025,
#                             ncc = TRUE)
#
# head(sim_scenarios)
#
# set.seed(1234)
# sim_results <- sim_study_par(nsim = 1000, scenarios = sim_scenarios, arms = 4,
#                              models = c("fixmodel", "sepmodel", "poolmodel"), endpoint = "cont")
#
# head(sim_results)
#
# ggplot(sim_results, aes(x=lambda0, y=reject_h0, color=model)) +
#   geom_point() +
#   geom_line() +
#   facet_grid(~ trend) +
#   geom_hline(aes(yintercept = 0.025), linetype = "dotted") +
#   labs(x="Strength of time trend", y="Type I error", color="Analysis approach") +
#   theme_bw()
# #ggsave("t1e.png", width = 7, height = 5)
#
# ggplot(sim_results, aes(x=lambda0, y=bias, color=model)) +
#   geom_point() +
#   geom_line() +
#   facet_grid(~ trend) +
#   geom_hline(aes(yintercept = 0), linetype = "dotted") +
#   labs(x="Strength of time trend", y="Bias", color="Analysis approach") +
#   theme_bw()
# #ggsave("bias.png", width = 7, height = 5)
#
# ggplot(sim_results, aes(x=lambda0, y=MSE, color=model)) +
#   geom_point() +
#   geom_line() +
#   facet_grid(~ trend) +
#   labs(x="Strength of time trend", y="MSE", color="Analysis approach") +
#   theme_bw()
# #ggsave("mse.png", width = 7, height = 5)


##################################################################################

# Binary case:

lambda_values <- rep(seq(-0.5, 0.5, length.out = 9), 2)
sim_scenarios <- data.frame(num_arms = 4,
                            n_arm = 250,
                            d1 = 250*0,
                            d2 = 250*1,
                            d3 = 250*2,
                            d4 = 250*3,
                            period_blocks = 2,
                            p0 = 0.7,
                            OR1 = 1,
                            OR2 = 1,
                            OR3 = 1,
                            OR4 = 1,
                            lambda0 = lambda_values,
                            lambda1 = lambda_values,
                            lambda2 = lambda_values,
                            lambda3 = lambda_values,
                            lambda4 = lambda_values,
                            trend = c(rep("linear", 9), rep("stepwise_2", 9)),
                            alpha = 0.025,
                            ncc = TRUE)

head(sim_scenarios)

set.seed(1234)
sim_results <- sim_study_par(nsim = 1000, scenarios = sim_scenarios, arms = 4,
                             models = c("fixmodel", "sepmodel", "poolmodel"), endpoint = "bin")

head(sim_results)

ggplot(sim_results, aes(x=lambda0, y=reject_h0, color=model)) +
  geom_point() +
  geom_line() +
  facet_grid(~ trend) +
  geom_hline(aes(yintercept = 0.025), linetype = "dotted") +
  labs(x="Strength of time trend", y="Type I error", color="Analysis approach") +
  theme_bw()
#ggsave("t1e.png", width = 7, height = 5)

ggplot(sim_results, aes(x=lambda0, y=bias, color=model)) +
  geom_point() +
  geom_line() +
  facet_grid(~ trend) +
  geom_hline(aes(yintercept = 0), linetype = "dotted") +
  labs(x="Strength of time trend", y="Bias", color="Analysis approach") +
  theme_bw()
#ggsave("bias.png", width = 7, height = 5)

ggplot(sim_results, aes(x=lambda0, y=MSE, color=model)) +
  geom_point() +
  geom_line() +
  facet_grid(~ trend) +
  labs(x="Strength of time trend", y="MSE", color="Analysis approach") +
  theme_bw()
#ggsave("mse.png", width = 7, height = 5)