Model the drug adherence using either a binomial probability distribution or
a markov chain model based on the probability of staying adherent and of
becoming adherent once non-adherent.
Usage
new_adherence(
n = 100,
type = c("markov", "binomial"),
p_markov_remain_ad = 0.75,
p_markov_become_ad = 0.75,
p_binom = 0.7
)
Arguments
- n
number of occasions to simulate
- type
type of adherence simulation, either "markov" or "binomial"
- p_markov_remain_ad
markov probability of staying adherent
- p_markov_become_ad
markov probability of going from non-adherent
to adherent state
- p_binom
binomial probability of being adherent
Value
Returns a vector of length `n`
containing values 0 (non-adherent) or 1 (adherent).
Numeric vector of length n