set_simul_pars: Set simulation parameters of a study group.

Description

This function provides input arguments for each study group needed to simulate data with simulate_data(). simulate_data() generates data for a two-arms clinical trial with longitudinal continuous outcomes and two intercurrent events (ICEs). ICE1 may be thought of as a discontinuation from study treatment due to study drug or condition related (SDCR) reasons. ICE2 may be thought of as discontinuation from study treatment due to uninformative study drop-out, i.e. due to not study drug or condition related (NSDCR) reasons and outcome data after ICE2 is always missing.

Usage

set_simul_pars(
  mu,
  sigma,
  n,
  prob_ice1 = 0,
  or_outcome_ice1 = 1,
  prob_post_ice1_dropout = 0,
  prob_ice2 = 0,
  prob_miss = 0
)

Value

A simul_pars object which is a named list containing the simulation parameters.

Arguments

mu: Numeric vector describing the mean outcome trajectory at each visit (including baseline) assuming no ICEs.
sigma: Covariance matrix of the outcome trajectory assuming no ICEs.
n: Number of subjects belonging to the group.
prob_ice1: Numeric vector that specifies the probability of experiencing ICE1 (discontinuation from study treatment due to SDCR reasons) after each visit for a subject with observed outcome at that visit equal to the mean at baseline (mu[1]). If a single numeric is provided, then the same probability is applied to each visit.
or_outcome_ice1: Numeric value that specifies the odds ratio of experiencing ICE1 after each visit corresponding to a +1 higher value of the observed outcome at that visit.
prob_post_ice1_dropout: Numeric value that specifies the probability of study drop-out following ICE1. If a subject is simulated to drop-out after ICE1, all outcomes after ICE1 are set to missing.
prob_ice2: Numeric that specifies an additional probability that a post-baseline visit is affected by study drop-out. Outcome data at the subject's first simulated visit affected by study drop-out and all subsequent visits are set to missing. This generates a second intercurrent event ICE2, which may be thought as treatment discontinuation due to NSDCR reasons with subsequent drop-out. If for a subject, both ICE1 and ICE2 are simulated to occur, then it is assumed that only the earlier of them counts. In case both ICEs are simulated to occur at the same time, it is assumed that ICE1 counts. This means that a single subject can experience either ICE1 or ICE2, but not both of them.
prob_miss: Numeric value that specifies an additional probability for a given post-baseline observation to be missing. This can be used to produce "intermittent" missing values which are not associated with any ICE.

Details

For the details, please see simulate_data().

Examples

Run this code

# Simulation parameters for a group with 5 visits (including baseline)
time <- c(0, 3, 6, 9, 12)

# Mean outcome trajectory
mu <- c(50.0, 52.5, 55.0, 57.5, 60.0)

# Covariance matrix implied by a random intercept and slope model
sd_error <- 2.5
covRE <- rbind(
    c(25.0, 6.25),
    c(6.25, 25.0)
)
sigma <- cbind(1, time / 12) %*% covRE %*% rbind(1, time / 12) +
    diag(sd_error^2, nrow = length(time))

set_simul_pars(
    mu = mu,
    sigma = sigma,
    n = 100,
    prob_ice1 = 0.03,
    or_outcome_ice1 = 1.10,
    prob_post_ice1_dropout = 0.5
)