inf_tci_pop: Calculate infusion rates to reach target values within a...

In jarretrt/tci: Target Controlled Infusion (TCI)

Calculate infusion rates to reach target values within a population

Description

This function will calculate infusion rates required to reach targets at a specified position (e.g., median, 25th percentile) of the response distribution in a population. The user supplies a 'poppkmod' object, a set of values to be obtained, a set of times at which to reach them, and a function to apply to the distribution of responses.

Usage

inf_tci_pop(
  mod,
  target_vals,
  target_tms,
  pop_fn = median,
  dtm = 1/6,
  inf_duration = NULL,
  cmpt = NULL,
  inittm = 0,
  maxrt = 1e+05
)

Arguments

mod	Object with class 'poppkmod', created by 'poppkmod()'.
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.
pop_fn	Function to apply to distribution of response values. Defaults to setting median value equal to targets.
dtm	TCI update frequency. Defaults to 1/6, corresponding to 10-second intervals if model parameters are in terms of minutes.
inf_duration	Optional parameter to describe duration of infusions. Can be less than or equal to 'dtm'. Defaults to 'dtm' if unspecified.
cmpt	Compartment used for targeting. Defaults to PD response if applicable and effect-site compartment if not.
inittm	Initial time to start TCI algorithm. Cannot be greater than the minimum value of 'target_tms'.
maxrt	Maximum infusion rate.

Examples

nid = 100
data <- data.frame(ID = 1:nid, AGE = sample(10:70, nid, replace = TRUE),
TBW = sample(40:90, nid, TRUE), HGT = sample(130:210, nid, TRUE),
MALE = sample(c(TRUE,FALSE),nid,TRUE))
elvd_mod <- poppkmod(data, drug = "ppf", model = "eleveld")
# calculate infusions to keep 90% of patients below 70
inf <- inf_tci_pop(elvd_mod, target_vals = c(70,70), target_tms = c(0,50),
pop_fn = function(...) quantile(..., 0.9), dtm = 10)
tms <- seq(0,50,0.1)
resp <- as.data.frame(predict(elvd_mod, inf, tms))
ggplot(resp, aes(x = time, y = pdresp, group = id)) +
geom_line(alpha = 0.1) +
geom_hline(yintercept = 70)

jarretrt/tci documentation built on Jan. 26, 2023, 3:37 p.m.