data_generator {cities}

data_generator

Description

Helper function to simulate single clinical trial

Usage

data_generator(
  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,
  seed_val,
  reference_id,
  plot_po = FALSE,
  up_good = "Up",
  threshold,
  delta_adjustment_in,
  covariate_df
)

Arguments

n_patient_vector

Vector of number of patients

p_loe_max

The maximum probability of discontinuing due to LoE

z_l_loe

The lower (or left) threshold of the LoE curve

z_u_loe

The upper (or right) threshold of the LoE curve

p_ee_max

The maximum probability of discontinuing due to EE

z_l_ee

The lower (or left) threshold of the EE curve

z_u_ee

The upper (or right) threshold of the EE curve

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.

p_admin

Vector of probabilities of discontinuing due to admin reasons

rate_dc_ae

Vector of probabilities of observing at least one adverse event

prob_ae

Vector of proportions of discontinuing due to adverse event

seed_val

Starting seed value

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

plot_po

TRUE, if plotting data only. Otherwise, set to FALSE

up_good

"Up" if higher outcome values indicate better responses

threshold

Value to dichotomize continuous outcomes on

delta_adjustment_in

Vector of delta adjustment values or NA if none. E.g. (2,3,1) when reference_id = 1 means no delta adjustment on arm 1 (even though 2 was supplied, but since arm 1 is the reference arm, this will be defaulted to 0 regardless), 3 on arm 2 and 1 on arm 3.

covariate_df

Matrix or dataframe of covariates. Set NA if using default covariates, which comprises one continuous (standard normal) and binary (bernoulli with prob 0.5) 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.

Value

List of dataframes of estimands and simulated data, including delta adjusted ones if requested:

estimand_mean

List of means of the FULL, S_++, S_*+ and PP estimands

estimand_sd

List of standard deviations of the FULL, S_++, S_*+ and PP estimands

dc_mean_list

List of proportions of discontinuations

observed_df

Dataframe of the observed outcomes

po_df

Dataframe of the potential outcomes

ir_df

Dataframe of the outcomes that have been adjusted via immediate reference (IR) or delta adjustment (Delta) for treatment policy estimands. The IR outcomes are labelled as ir_data while the delta adjusted outcomes are labelled as delta_data. The delta adjusted outcomes will only be available if the correct inputs for delta_adjustment_in are provided

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)

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 = static_output)

data_out = data_generator(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,
delta_adjustment_in = delta_adjustment_in,
covariate_df = covariate_df)