Simulate intercurrent event

Usage

simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)

Arguments

outcome: Numeric variable that specifies the longitudinal outcome for a single group.
visits: Factor variable that specifies the visit of each assessment.
ids: Factor variable that specifies the id of each subject.
prob_ice: 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.
or_outcome_ice: Numeric value that specifies the odds ratio of the ICE corresponding to a +1 higher value of the outcome at the visit.
baseline_mean: Mean outcome value at baseline.

Value

A binary variable that takes value 1 if the corresponding outcome is affected by the ICE and 0 otherwise.

Details

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).