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 (NSDRC) 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
)
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 NSDRC 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()
.