Simulate intercurrent event
simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)A binary variable that takes value 1 if the corresponding outcome is affected
by the ICE and 0 otherwise.
Numeric variable that specifies the longitudinal outcome for a single group.
Factor variable that specifies the visit of each assessment.
Factor variable that specifies the id of each subject.
Numeric vector that specifies for each visit the probability of experiencing the ICE after the current visit for a subject with outcome equal to the mean at baseline. If a single numeric is provided, then the same probability is applied to each visit.
Numeric value that specifies the odds ratio of the ICE corresponding to a +1 higher value of the outcome at the visit.
Mean outcome value at baseline.
The probability of the ICE after each visit is modeled according to the following
logistic regression model:
~ 1 + I(visit == 0) + ... + I(visit == n_visits-1) + I((x-alpha)) where:
n_visits is the number of visits (including baseline).
alpha is the baseline outcome mean set via argument baseline_mean.
The term I((x-alpha)) specifies the dependency of the probability of the ICE on the current
outcome value.
The corresponding regression coefficients of the logistic model are defined as follows:
The intercept is set to 0, the coefficients corresponding to discontinuation after each visit
for a subject with outcome equal to
the mean at baseline are set according to parameter or_outcome_ice,
and the regression coefficient associated with the covariate I((x-alpha)) is set to
log(or_outcome_ice).