new_adherence: Probabilistically model adherence

In PKPDsim: Tools for Performing Pharmacokinetic-Pharmacodynamic Simulations

Probabilistically model adherence

Description

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

PKPDsim documentation built on March 7, 2023, 5:40 p.m.