simulate_olc {tci}

Simulate open-loop control using TCI

Description

Simulate open-loop control using TCI for 'pkmod' or 'poppkmod' objects. Infusion rates are calculated using 'pkmod_prior' to reach 'target_vals' at 'target_tms'. Data values are simulated using 'pkmod_true' at 'obs_tms'. 'pkmod_prior' and 'pkmod_true' do not need to have the same structure, but are required to have the same number of IDs (i.e., N) if 'poppkmod' objects are used.

Usage

simulate_olc(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  seed = NULL
)

Arguments

pkmod_prior

'pkmod' object describing a PK/PK-PD model that is used to calculate TCI infusion rates and is updated as data are simulated and incorporated. Must have an associated Omega matrix.

pkmod_true

‘pkmod' object describing the patient’s "true" response. This model will be used to simulate observations.

target_vals

A vector of numeric values indicating PK or PD targets for TCI algorithm.

target_tms

A vector of numeric values indicating times at which the TCI algorithm should begin targeting each value.

obs_tms

Times at which data values should be simulated from 'pkmod_true'.

type

Type of TCI algorithm to be used. Options are "plasma" and "effect". Defaults to "effect". Will be overwritten if 'custom_alg' is non-null.

custom_alg

Custom TCI algorithm to overwrite default plasma- or effect-site targeting.

resp_bounds

Optional vector of two values indicating minimum and maximum values possible for the response.

seed

An integer used to initialize the random number generator.

Examples

data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)
sim <- simulate_olc(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms)
len <- 500
tms <- seq(0,10,length.out = len)
resp <- data.frame(rbind(predict(pkmod_true, sim$inf, tms),
predict(pkmod_prior, sim$inf, tms)))
resp$type = c(rep("true",len*5),rep("prior",len*5))
library(ggplot2)
ggplot(resp) + geom_line(aes(x = time, y = pdresp, color = factor(id))) + facet_wrap(~type) +
  labs(x = "Hours", y = "Bispectral Index") + theme_bw() +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE)