plot_means {cities}

plot_means

Description

Plots the means of simulation parameters.

Usage

plot_means(
  n_patient_vector,
  timepoints,
  pacf_list,
  sigma_ar_vec,
  mean_list,
  beta_list,
  reference_id,
  seed_val,
  threshold,
  total_data,
  covariate_df,
  static_output = FALSE
)

Arguments

n_patient_vector

Vector of number of patients

timepoints

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

pacf_list

List of pacf vectors

sigma_ar_vec

Vector of variances per arm associated with list of pacf vectors

mean_list

List of vectors of means per arm

beta_list

List of vectors of beta coefficients per arm. All vectors must have the same length and must be the same as the number of columns for the covariate_df.

reference_id

ID for pairwise comparisons, e.g. for three arms, if reference_id=1, then arms 2 and 3 will be compared only to arm 1

seed_val

Starting seed value

threshold

Value to dichotomize continuous outcomes on

total_data

Total number of clinical trials to simulate

covariate_df

Matrix or dataframe of covariates. Rows correspond to the total number of subjects. Order matters, For instance, if you want to simulate a trial with 3 arms, each of size 30,50 and 80, then covariate_df would have 30+50+80 rows such that the first 30 rows are covariates for arm 1, the next 50 rows are covariates for arm 2 and the last 80 rows are covariates for arm 3.

static_output

TRUE, if static and FALSE if dynamic plot is requested

Value

The plot of raw means.

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

# 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

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)