plot_dc {cities}

plot_dc

Description

Plots the discontinuation rates by timepoints

Usage

plot_dc(
  data_out,
  total_data,
  timepoints,
  normal_output = TRUE,
  static_output = FALSE,
  greyscale = FALSE
)

Arguments

data_out

The output from data_generator_loop()

total_data

Total number of clinical trials to simulate

timepoints

Vector of timepoints (e.g. weeks, days, time indices)

normal_output

TRUE if both plots and numeric values of estimands are requested. FALSE if only plots are requested

static_output

TRUE if static mode requested and FALSE if dynamic plot is requested

greyscale

TRUE if greyscale requested and FALSE for color

Value

Plot and dataframe of proportion of discontinuations.

Examples

total_data = 3
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
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
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
data_out = data_generator_loop(n_patient_vector = n_patient_vector,
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, timepoints = timepoints,
pacf_list = pacf_list, sigma_ar_vec = sigma_ar_vec, mean_list = mean_list,
beta_list = beta_list, p_admin = p_admin, rate_dc_ae = rate_dc_ae,
prob_ae = prob_ae, seed_val = starting_seed_val, reference_id = reference_id,
plot_po = FALSE, up_good = up_good, threshold = threshold,
total_data = total_data, delta_adjustment_in = delta_adjustment_in,
covariate_df = 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)

dc_out = plot_dc(data_out = data_out, total_data = total_data,
timepoints = timepoints, static_output = static_output)