Event Table Function

Usage

et(x, ..., envir = parent.frame())

# S3 method for class 'rxode2'
et(x, ..., envir = parent.frame())

# S3 method for class '`function`'
et(x, ..., envir = parent.frame())

# S3 method for class 'rxUi'
et(x, ..., envir = parent.frame())

# S3 method for class 'rxSolve'
et(x, ..., envir = parent.frame())

# S3 method for class 'rxParams'
et(x, ..., envir = parent.frame())

# Default S3 method
et(
  x,
  ...,
  time,
  amt,
  evid,
  cmt,
  ii,
  addl,
  ss,
  rate,
  dur,
  until,
  id,
  amountUnits,
  timeUnits,
  addSampling,
  envir = parent.frame(),
  by = NULL,
  length.out = NULL
)

Arguments

x

This is the first argument supplied to the event table. This is named to allow et to be used in a pipe-line with arbitrary objects.

...

Times or event tables. They can also be one of the named arguments below.

envir

the environment in which expr is to be evaluated. May also be NULL, a list, a data frame, a pairlist or an integer as specified to sys.call.

time

Time is the time of the dose or the sampling times. This can also be unspecified and is determined by the object type (list or numeric/integer).

amt

Amount of the dose. If specified, this assumes a dosing record, instead of a sampling record.

evid

Event ID; This can be:

Numeric Value	Description
0	An observation. This can also be specified as `evid=obs`
1	A dose observation. This can also be specified as `evid=dose`
2	A non-dose event. This can also be specified as `evid=other`
3	A reset event. This can also be specified as `evid=reset`.
4	Dose and reset event. This can also be specified as `evid=doseReset` or `evid=resetDose`

Note a reset event resets all the compartment values to zero and turns off all infusions.

cmt

Compartment name or number. If a number, this is an integer starting at 1. Negative compartments turn off a compartment. If the compartment is a name, the compartment name is changed to the correct state/compartment number before running the simulation. For a compartment named "-cmt" the compartment is turned off.

Can also specify `cmt` as `dosing.to`,
`dose.to`, `doseTo`, `dosingTo`, and
`state`.

ii

When specifying a dose, this is the inter-dose interval for ss, addl and until options (described below).

addl

The number of additional doses at a inter-dose interval after one dose.

ss

Steady state flag; It can be one of:

Value	Description
0	This dose is not a steady state dose
1	This dose is a steady state dose with the between/inter-dose interval of `ii`
2	Superposition steady state

When ss=2 the steady state dose that uses the super-position principle to allow more complex steady states, like 10 mg in the morning and 20 mg at night, or dosing at 8 am 12 pm and 8 pm instead of every 12 hours. Since it uses the super positioning principle, it only makes sense when you know the kinetics are linear.

All other values of SS are currently invalid.

rate

When positive, this is the rate of infusion. Otherwise:

Value	Description
0	No infusion is on this record
-1	Modeled rate (in rxode2:`rate(cmt) =`); Can be `et(rate=model)`.
-2	Modeled duration (in rxode2: `dur(cmt) =`); Can be`et(dur=model)` or `et(rate=dur)`.

When a modeled bioavailability is applied to positive rates (rate > 0), the duration of infusion is changed. This is because the data specify the rate and amount, the only think that modeled bioavailability can affect is duration.

If instead you want the modeled bioavailability to increase the rate of infusion instead of the duration of infusion, specify the dur instead or model the duration with rate=2.

dur

Duration of infusion. When amt and dur are specified the rate is calculated from the two data items. When dur is specified instead of rate, the bioavailability changes will increase rate instead of duration.

until

This is the time until the dosing should end. It can be an easier way to figure out how many additional doses are needed over your sampling period.

id

A integer vector of IDs to add or remove from the event table. If the event table is identical for each ID, then you may expand it to include all the IDs in this vector. All the negative IDs in this vector will be removed.

amountUnits

The units for the dosing records (amt)

timeUnits

The units for the time records (time)

addSampling

This is a boolean indicating if a sampling time should be added at the same time as a dosing time. By default this is FALSE.

by

number: increment of the sequence.

length.out

desired length of the sequence. A non-negative number, which for seq and seq.int will be rounded up if fractional.

Value

A new event table

References

Wang W, Hallow K, James D (2015). "A Tutorial on rxode2: Simulating Differential Equation Pharmacometric Models in R." CPT: Pharmacometrics and Systems Pharmacology, 5(1), 3-10. ISSN 2163-8306

Author

Matthew L Fidler, Wenping Wang

Examples


if (FALSE) { # \dontrun{

library(rxode2)
library(units)

# Model from rxode2 tutorial
# Using a nlmixr2 style function

mod1 <-function(){
  ini({
    KA <- 2.94E-01
    CL <- 1.86E+01
    V2 <- 4.02E+01
    Q <- 1.05E+01
    V3 <- 2.97E+02
    Kin <- 1
    Kout <- 1
    EC50 <- 200
  })
 model({
    C2 <- centr/V2
    C3 <- peri/V3
    d/dt(depot) <- -KA*depot
    d/dt(centr) <- KA*depot - CL*C2 - Q*C2 + Q*C3
    d/dt(peri)  <-                    Q*C2 - Q*C3
    d/dt(eff)   <- Kin - Kout*(1-C2/(EC50+C2))*eff
 })
}

## These are making the more complex regimens of the rxode2 tutorial

## bid for 5 days
bid <- et(timeUnits="hr") |>
       et(amt=10000,ii=12,until=set_units(5, "days"))

## qd for 5 days
qd <- et(timeUnits="hr") |>
      et(amt=20000,ii=24,until=set_units(5, "days"))

## bid for 5 days followed by qd for 5 days

et <- seq(bid,qd) |>
      et(seq(0,11*24,length.out=100))

bidQd <- rxSolve(mod1, et)

plot(bidQd, C2)


## Now Infusion for 5 days followed by oral for 5 days

##  note you can dose to a named compartment instead of using the compartment number
infusion <- et(timeUnits = "hr") |>
      et(amt=10000, rate=5000, ii=24, until=set_units(5, "days"), cmt="centr")


qd <- et(timeUnits = "hr") |>
  et(amt=10000, ii=24, until=set_units(5, "days"), cmt="depot")

et <- seq(infusion,qd)

infusionQd <- rxSolve(mod1, et)

plot(infusionQd, C2)

## 2wk-on, 1wk-off

qd <- et(timeUnits = "hr") |>
      et(amt=10000, ii=24, until=set_units(2, "weeks"), cmt="depot")

et <- seq(qd, set_units(1,"weeks"), qd) |>
     add.sampling(set_units(seq(0, 5.5,by=0.005),weeks))

wkOnOff <- rxSolve(mod1, et)

plot(wkOnOff, C2)

## You can also repeat the cycle easily with the rep function

qd <-et(timeUnits = "hr") |>
     et(amt=10000, ii=24, until=set_units(2, "weeks"), cmt="depot")

et <- etRep(qd, times=4, wait=set_units(1,"weeks")) |>
      add.sampling(set_units(seq(0, 12.5,by=0.005),weeks))

repCycle4 <- rxSolve(mod1, et)

plot(repCycle4, C2)

} # }