mic_stat: Time Above Pharmacokinetic Threshold

In hlweeks/pkpredict: Compartment Model Implementation for Pharmacokinetic Analysis

Time Above Pharmacokinetic Threshold

Description

Compute an estimate of pharmacodynamic target attainment, typically defined to be some multiple of the minimum inhibitory concentration of the infecting microorganism.

Usage

mic_stat(
  ivt,
  th,
  dat = data.frame(),
  pars = c(getOption("pkpredict.pip.default.prior")$log_pk_mean,
    getOption("pkpredict.pip.default.prior")$log_err_mean),
  cod = 12,
  timeint = NULL,
  conf.level = 0.95,
  mcmc = FALSE,
  nreps = 5000,
  nburnin = 2000,
  nthin = 10,
  seed = NULL,
  shiny = FALSE,
  ...
)

Arguments

ivt	List with containing start of infusion times (h), end of infusion times (h), and rate of infusion (g/h) at each dose. First infusion within ivt must be a true initial infusion, i.e. concentration of the drug in the patient's system must be 0 prior to that time.
th	Threshold value for effective treatment - check units
dat	data
pars	Vector of pharmacokinetic parameters of length 5: (log(v_1), log(k_10), log(k_12), log(k_21), log(err))
cod	Length of time after end of last dose to consider
timeint	Vector with two elements indicating the start and end of the time interval over which the statistic is computed. By default covers the full dosing period + cod
conf.level	Desired confidence level of the interval
mcmc	logical: should estimate of time above threshold be computed using MCMC (false = laplace approximation)
nreps	mcmc replications
nburnin	mcmc burn in iterations
nthin	mcmc thinning interval
seed	seed for replication
shiny	is shiny being used
...	additional arguments (e.g., 'mu', 'sig', 'ler_mean', 'ler_sdev' for changing the PK parameter prior mean, variance-covariance matrix and error prior mean and standard deviation, respectively)

Value

Fraction of time spent above the specified threshold within a certain time interval (by default: time of first dose through cod hours after end of the last dose).

hlweeks/pkpredict documentation built on Oct. 29, 2023, 6:08 a.m.