simulate_ice: Simulate intercurrent event

In rbmi: Reference Based Multiple Imputation

Simulate intercurrent event

Description

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.

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).

Value

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

rbmi documentation built on Nov. 24, 2023, 5:11 p.m.