cities (version 0.1.3)

simulated_data_output: simulated_data_output

Description

Helper function to combine simulated data

Usage

simulated_data_output(
  n_patient_cumsum,
  i,
  first_patient,
  data_in,
  covariate_df,
  timepoints,
  beta_list,
  seed_val,
  potential_outcomes = FALSE,
  observed_indicator = NA
)

Value

Dataframe of for either potential outcomes, observed outcomes, outcomes with immediate reference assumption or delta adjustment assumption

Arguments

n_patient_cumsum

Vector of number of patients

i

Index for arm

first_patient

Index for first patient of arm

data_in

Simulated data from data_generator()

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.

timepoints

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

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.

seed_val

Current seed value

potential_outcomes

TRUE if data to be combined is for potential outcomes, and FALSE otherwise

observed_indicator

Dataframe containing which subjects/arms/timepoints were observed (necessary for potential outcomes), else default to NA

Examples

n_patient_ctrl = 120
n_patient_expt = 150
n_patient_vector = c(n_patient_ctrl, n_patient_expt)
n_patient_cumsum = cumsum(n_patient_vector)
total_patients = sum(n_patient_vector)
timepoints = c(0,24,48,72,96,120,144)
data_in = matrix(rnorm(length(timepoints)*n_patient_ctrl), ncol = length(timepoints))
i = 1
first_patient = 1
covariate_df = data.frame(continuous  = rnorm(n = total_patients, mean = 0, sd = 1),
binary = rbinom(n = total_patients, size = 1, prob = 0.5))
beta_list = NA
seed_val = 1
potential_outcomes = FALSE
observed_indicator = NA
simulated_data_output(n_patient_cumsum = n_patient_cumsum, i = i,
first_patient = first_patient, data_in = data_in, covariate_df = covariate_df,
timepoints = timepoints, beta_list = beta_list, seed_val = seed_val,
potential_outcomes = FALSE, observed_indicator = NA)