Chapter 7 RxODE events
7.1 RxODE event tables
In general, RxODE event tables follow NONMEM dataset convention with the exceptions:
- The compartment data item (
cmt
) can be a string/factor with compartment names- You may turn off a compartment with a negative compartment number or “-cmt” where cmt is the compartment name.
- The compartment data item (
cmt
) can still be a number, the number of the compartment is defined by the appearance of the compartment name in the model. This can be tedious to count, so you can specify compartment numbers easier by using thecmt(cmtName)
at the beginning of the model.
- An additional column,
dur
can specify the duration of infusions;- Bioavailability changes will change the rate of infusion since
dur
/amt
are fixed in the input data. - Similarly, when specifying
rate
/amt
for an infusion, the bioavailability will change the infusion duration sincerate
/amt
are fixed in the input data.
- Bioavailability changes will change the rate of infusion since
- Some infrequent NONMEM columns are not supported:
pcmt
,call
. - Additional events are supported:
evid=5
or replace event; This replaces the value of a compartment with the value specified in theamt
column. This is equivalent todeSolve
=replace
.evid=6
or multiply event; This multiplies the value in the compartment with the value specified by theamt
column. This is equivalent todeSolve
=multiply
.
Here are the legal entries to a data table:
Data Item | Meaning | Notes |
---|---|---|
id | Individual identifier | Can be a integer, factor, character, or numeric |
time | Individual time | Numeric for each time. |
amt | dose amount | Positive for doses zero/NA for observations |
rate | infusion rate | When specified the infusion duration will be dur=amt/rate |
rate = -1, rate modeled; rate = -2, duration modeled | ||
dur | infusion duration | When specified the infusion rate will be rate = amt/dur |
evid | event ID | 0=Observation; 1=Dose; 2=Other; 3=Reset; 4=Reset+Dose; 5=Replace; 6=Multiply |
cmt | Compartment | Represents compartment #/name for dose/observation |
ss | Steady State Flag | 0 = non-steady-state; 1=steady state; 2=steady state +prior states |
ii | Inter-dose Interval | Time between doses. |
addl | # of additional doses | Number of doses like the current dose. |
Other notes:
- The
evid
can be the classic RxODE (described here) or theNONMEM
-styleevid
described above. NONMEM
’sDV
is not required;RxODE
is a ODE solving framework.NONMEM
’sMDV
is not required, since it is captured inEVID
.- Instead of
NONMEM
-compatible data, it can acceptdeSolve
compatible data-frames.
When returning the RxODE solved data-set there are a few additional
event ids (EVID
) that you may see depending on the solving options:
EVID = -1
is when a modeled rate ends (corresponds torate = -1
)EVID = -2
is when a modeled duration ends (corresponds torate=-2
)EVID = -10
when a rate specified zero-order infusion ends (corresponds torate > 0
)EVID = -20
when a duration specified zero-order infusion ends (corresponds todur > 0
)EVID = 101, 102, 103,...
These correspond to the1
,2
,3
, … modeled time (mtime
).
These can only be accessed when solving with the option combination
addDosing=TRUE
and subsetNonmem=FALSE
. If you want to see the
classic EVID
equivalents you can use addDosing=NA
.
To illustrate the event types we will use the model from the original RxODE tutorial.
library(RxODE)
### Model from RxODE tutorial
RxODE({
m1 <-2.94E-01;
KA=1.86E+01;
CL=4.02E+01;
V2=1.05E+01;
Q=2.97E+02;
V3=1;
Kin=1;
Kout=200;
EC50=## Added modeled bioavaiblity, duration and rate
1;
fdepot = 8;
durDepot = 1250;
rateDepot = centr/V2;
C2 = peri/V3;
C3 =/dt(depot) =-KA*depot;
df(depot) = fdepot
dur(depot) = durDepot
rate(depot) = rateDepot
/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;
deff(0) = 1
});
7.2 Bolus/Additive Doses
A bolus dose is the default type of dose in RxODE and only requires
the amt
/dose
. Note that this uses the convenience function et()
described in the RxODE event tables
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 5
#> time amt ii addl evid
#> [h] <dbl> [h] <int> <evid>
#> 1 0.0000000 NA NA NA 0:Observation
#> 2 0.0000000 10000 12 2 1:Dose (Add)
#> 3 0.2424242 NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA 0:Observation
#> # … with 91 more rows
rxSolve(m1, ev) %>% plot(C2) +
xlab("Time")
7.3 Infusion Doses
There are a few different type of infusions that RxODE supports:
- Constant Rate Infusion (
rate
) - Constant Duration Infusion (
dur
) - Estimated Rate of Infusion
- Estimated Duration of Infusion
7.3.1 Constant Infusion (in terms of duration and rate)
The next type of event is an infusion; There are two ways to specify
an infusion; The first is the dur
keyword.
An example of this is:
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24, dur=8) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 6
#> time amt ii addl evid dur
#> [h] <dbl> [h] <int> <evid> [h]
#> 1 0.0000000 NA NA NA 0:Observation NA
#> 2 0.0000000 10000 12 2 1:Dose (Add) 8
#> 3 0.2424242 NA NA NA 0:Observation NA
#> 4 0.4848485 NA NA NA 0:Observation NA
#> 5 0.7272727 NA NA NA 0:Observation NA
#> 6 0.9696970 NA NA NA 0:Observation NA
#> 7 1.2121212 NA NA NA 0:Observation NA
#> 8 1.4545455 NA NA NA 0:Observation NA
#> 9 1.6969697 NA NA NA 0:Observation NA
#> 10 1.9393939 NA NA NA 0:Observation NA
#> # … with 91 more rows
rxSolve(m1, ev) %>% plot(depot, C2) +
xlab("Time")
It can be also specified by the rate
component:
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24, rate=10000/8) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 6
#> time amt rate ii addl evid
#> [h] <dbl> <rate/dur> [h] <int> <evid>
#> 1 0.0000000 NA NA NA NA 0:Observation
#> 2 0.0000000 10000 1250 12 2 1:Dose (Add)
#> 3 0.2424242 NA NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA NA 0:Observation
#> # … with 91 more rows
rxSolve(m1, ev) %>% plot(depot, C2) +
xlab("Time")
These are the same with the exception of how bioavailability changes the infusion.
In the case of modeling rate
, a bioavailability decrease,
decreases the infusion duration, as in NONMEM. For example:
rxSolve(m1, ev, c(fdepot=0.25)) %>% plot(depot, C2) +
xlab("Time")
Similarly increasing the bioavailability increases the infusion duration.
rxSolve(m1, ev, c(fdepot=1.25)) %>% plot(depot, C2) +
xlab("Time")
The rationale for this behavior is that the rate
and amt
are
specified by the event table, so the only thing that can change with a
bioavailability increase is the duration of the infusion.
If you specify the amt
and dur
components in the event table,
bioavailability changes affect the rate
of infusion.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24, dur=8) %>%
et(seq(0, 24, length.out=100))
You can see the side-by-side comparison of bioavailability changes
affecting rate
instead of duration with these records in the
following plots:
library(ggplot2)
library(patchwork)
rxSolve(m1, ev, c(fdepot=1.25)) %>% plot(depot) +
p1 <- xlab("Time") + ylim(0,5000)
rxSolve(m1, ev, c(fdepot=0.25)) %>% plot(depot) +
p2 <- xlab("Time")+ ylim(0,5000)
### Use patchwork syntax to combine plots
* p2 p1
7.3.2 Modeled Rate and Duration of Infusion
You can model the duration, which is equivalent to NONMEM’s
rate=-2
. As a mnemonic you can use the dur=model
instead of
rate=-2
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24, dur=model) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 6
#> time amt rate ii addl evid
#> [h] <dbl> <rate/dur> [h] <int> <evid>
#> 1 0.0000000 NA NA NA NA 0:Observation
#> 2 0.0000000 10000 -2:dur 12 2 1:Dose (Add)
#> 3 0.2424242 NA NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA NA 0:Observation
#> # … with 91 more rows
rxSolve(m1, ev, c(durDepot=7)) %>% plot(depot, C2) +
xlab("Time")
Similarly, you may also model rate. This is equivalent to NONMEM’s
rate=-1
and is how RxODE’s event table specifies the data item as
well. You can also use rate=model
as a mnemonic:
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12,until=24, rate=model) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 6
#> time amt rate ii addl evid
#> [h] <dbl> <rate/dur> [h] <int> <evid>
#> 1 0.0000000 NA NA NA NA 0:Observation
#> 2 0.0000000 10000 -1:rate 12 2 1:Dose (Add)
#> 3 0.2424242 NA NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA NA 0:Observation
#> # … with 91 more rows
rxSolve(m1, ev, c(rateDepot=10000/3)) %>% plot(depot, C2) +
xlab("Time")
7.4 Steady State
These doses are solved until a steady state is reached with a constant inter-dose interval.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, ss=1) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 101 records ───────────────
#>
#> 1 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 101 x 5
#> time amt ii evid ss
#> [h] <dbl> [h] <evid> <int>
#> 1 0.0000000 NA NA 0:Observation NA
#> 2 0.0000000 10000 12 1:Dose (Add) 1
#> 3 0.2424242 NA NA 0:Observation NA
#> 4 0.4848485 NA NA 0:Observation NA
#> 5 0.7272727 NA NA 0:Observation NA
#> 6 0.9696970 NA NA 0:Observation NA
#> 7 1.2121212 NA NA 0:Observation NA
#> 8 1.4545455 NA NA 0:Observation NA
#> 9 1.6969697 NA NA 0:Observation NA
#> 10 1.9393939 NA NA 0:Observation NA
#> # … with 91 more rows
rxSolve(m1, ev) %>% plot(C2)
7.4.1 Steady state for complex dosing
By using the ss=2
flag, you can use the super-positioning principle
in linear kinetics to get steady state nonstandard dosing
(i.e. morning 100 mg vs evening 150 mg). This is done by:
- Saving all the state values
- Resetting all the states and solving the system to steady state
- Adding back all the prior state values
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=24, ss=1) %>%
et(time=12, amt=15000, ii=24, ss=2) %>%
et(time=24, amt=10000, ii=24, addl=3) %>%
et(time=36, amt=15000, ii=24, addl=3) %>%
et(seq(0, 64, length.out=500))
library(ggplot2)
rxSolve(m1, ev,maxsteps=10000) %>% plot(C2) +
annotate("rect", xmin=0, xmax=24, ymin=-Inf, ymax=Inf,
alpha=0.2) +
annotate("text", x=12.5, y=7,
label="Initial Steady State Period") +
annotate("text", x=44, y=7,
label="Steady State AM/PM dosing")
You can see that it takes a full dose cycle to reach the true complex steady state dosing.
7.4.2 Steady state for constant infusion or zero order processes
The last type of steady state that RxODE supports is steady-state constant infusion rate. This can be specified the same way as NONMEM, that is:
- No inter-dose interval
ii
=0
- A steady state dose, ie
ss
=1
- Either a positive rate (
rate
>0) or a estimated raterate
=-1
. - A zero dose, ie
amt
=0
- Once the steady-state constant infusion is achieved, the infusion is turned off when using this record, just like NONMEM.
Note that rate
=-2
where we model the duration of infusion doesn’t
make much sense since we are solving the infusion until steady state.
The duration is specified by the steady state solution.
Also note that bioavailability changes on this steady state infusion
also do not make sense because they neither change the rate
or the
duration of the steady state infusion. Hence modeled bioavailability
on this type of dosing event is ignored.
Here is an example:
et(timeUnits="hr") %>%
ev <- et(amt=0, ss=1,rate=10000/8)
rxSolve(m1, ev) %>% plot(C2, eff)
p1 <-
et(timeUnits="hr") %>%
ev <- et(amt=200000, rate=10000/8) %>%
et(0, 250, length.out=1000)
rxSolve(m1, ev) %>% plot(C2, eff)
p2 <-
library(patchwork)
/ p2 p1
Not only can this be used for PK, it can be used for steady-state disease processes.
7.5 Reset Events
Reset events are implemented by evid=3
or evid=reset
, for reset
and evid=4
for reset and dose.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, addl=3) %>%
et(time=6, evid=reset) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 102 records ───────────────
#>
#> 2 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 102 x 5
#> time amt ii addl evid
#> [h] <dbl> [h] <int> <evid>
#> 1 0.0000000 NA NA NA 0:Observation
#> 2 0.0000000 10000 12 3 1:Dose (Add)
#> 3 0.2424242 NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA 0:Observation
#> # … with 92 more rows
The solving show what happens in this system when the system is reset at 6 hours post-dose.
rxSolve(m1, ev) %>% plot(depot,C2, eff)
You can see all the compartments are reset to their initial values. The next dose start the dosing cycle over.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, addl=3) %>%
et(time=6, amt=10000, evid=4) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 102 records ───────────────
#>
#> 2 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 102 x 5
#> time amt ii addl evid
#> [h] <dbl> [h] <int> <evid>
#> 1 0.0000000 NA NA NA 0:Observation
#> 2 0.0000000 10000 12 3 1:Dose (Add)
#> 3 0.2424242 NA NA NA 0:Observation
#> 4 0.4848485 NA NA NA 0:Observation
#> 5 0.7272727 NA NA NA 0:Observation
#> 6 0.9696970 NA NA NA 0:Observation
#> 7 1.2121212 NA NA NA 0:Observation
#> 8 1.4545455 NA NA NA 0:Observation
#> 9 1.6969697 NA NA NA 0:Observation
#> 10 1.9393939 NA NA NA 0:Observation
#> # … with 92 more rows
In this case, the whole system is reset and the dose is given
rxSolve(m1, ev) %>% plot(depot,C2, eff)
7.6 Turning off compartments
You may also turn off a compartment, which is similar to a reset event.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, addl=3) %>%
et(time=6, cmt="-depot", evid=2) %>%
et(seq(0, 24, length.out=100))
ev
#> ──────────────── EventTable with 102 records ───────────────
#>
#> 2 dosing records (see x$get.dosing(); add with add.dosing or et)
#> 100 observation times (see x$get.sampling(); add with add.sampling or et)
#> multiple doses in `addl` columns, expand with x$expand(); or etExpand(x)
#> ── First part of x: ────────────────────────────────────────
#> # A tibble: 102 x 6
#> time cmt amt ii addl evid
#> [h] <chr> <dbl> [h] <int> <evid>
#> 1 0.0000000 (obs) NA NA NA 0:Observation
#> 2 0.0000000 (default) 10000 12 3 1:Dose (Add)
#> 3 0.2424242 (obs) NA NA NA 0:Observation
#> 4 0.4848485 (obs) NA NA NA 0:Observation
#> 5 0.7272727 (obs) NA NA NA 0:Observation
#> 6 0.9696970 (obs) NA NA NA 0:Observation
#> 7 1.2121212 (obs) NA NA NA 0:Observation
#> 8 1.4545455 (obs) NA NA NA 0:Observation
#> 9 1.6969697 (obs) NA NA NA 0:Observation
#> 10 1.9393939 (obs) NA NA NA 0:Observation
#> # … with 92 more rows
Solving shows what this does in the system:
rxSolve(m1, ev) %>% plot(depot,C2, eff)
In this case, the depot is turned off, and the depot compartment concentrations are set to the initial values but the other compartment concentrations/levels are not reset. When another dose to the depot is administered the depot compartment is turned back on.
Note that a dose to a compartment only turns back on the compartment that was dosed. Hence if you turn off the effect compartment, it continues to be off after another dose to the depot.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, addl=3) %>%
et(time=6, cmt="-eff", evid=2) %>%
et(seq(0, 24, length.out=100))
rxSolve(m1, ev) %>% plot(depot,C2, eff)
To turn back on the compartment, a zero-dose to the compartment or a evid=2 with the compartment would be needed.
et(timeUnits="hr") %>%
ev <- et(amt=10000, ii=12, addl=3) %>%
et(time=6, cmt="-eff", evid=2) %>%
et(time=12,cmt="eff",evid=2) %>%
et(seq(0, 24, length.out=100))
rxSolve(m1, ev) %>% plot(depot,C2, eff)
7.7 Classic RxODE events
Originally RxODE supported compound event IDs; RxODE still supports these parameters, but it is often more useful to use the the normal NONMEM dataset standard that is used by many modeling tools like NONMEM, Monolix and nlmixr, described in the RxODE event types article.
Classically, RxODE supported event coding in a single event id evid
described in the following table.
100+ cmt | Infusion/Event Flag | <99 Cmt | SS flag & Turning of Compartment |
---|---|---|---|
100+ cmt | 0 = bolus dose | < 99 cmt | 1 = dose |
1 = infusion (rate) | 10 = Steady state 1 (equivalent to SS=1) | ||
2 = infusion (dur) | 20 = Steady state 2 (equivalent to SS=2) | ||
6 = turn off modeled duration | 30 = Turn off a compartment (equivalent to -CMT w/EVID=2) | ||
7 = turn off modeled rate | |||
8 = turn on modeled duration | |||
9 = turn on modeled rate | |||
4 = replace event | |||
5 = multiply event |
The classic EVID concatenate the numbers in the above table, so an
infusion would to compartment 1 would be 10101
and an infusion to compartment 199 would be 119901
.
EVID = 0 (observations), EVID=2 (other type event) and EVID=3 are all supported. Internally an EVID=9 is a non-observation event and makes sure the system is initialized to zero; EVID=9 should not be manually set. EVID 10-99 represents modeled time interventions, similar to NONMEM’s MTIME. This along with amount (amt) and time columns specify the events in the ODE system.
For infusions specified with EVIDs > 100 the amt column represents the rate value.
For Infusion flags 1 and 2 +amt
turn on the infusion to a specific
compartment -amt
turn off the infusion to a specific
compartment. To specify a dose/duration you place the dosing records
at the time the duration starts or stops.
For modeled rate/duration infusion flags the on infusion flag must be followed by an off infusion record.
These number are concatenated together to form a full RxODE event ID, as shown in the following examples:
7.7.1 Bolus Dose Examples
A 100 bolus dose to compartment #1 at time 0
time | evid | amt |
---|---|---|
0 | 101 | 100 |
0.5 | 0 | 0 |
1 | 0 | 0 |
A 100 bolus dose to compartment #99 at time 0
time | evid | amt |
---|---|---|
0 | 9901 | 100 |
0.5 | 0 | 0 |
1 | 0 | 0 |
A 100 bolus dose to compartment #199 at time 0
time | evid | amt |
---|---|---|
0 | 109901 | 100 |
0.5 | 0 | 0 |
1 | 0 | 0 |
7.7.2 Infusion Event Examples
Bolus infusion with rate 50 to compartment 1 for 1.5 hr, (modeled bioavailability changes duration of infusion)
time | evid | amt |
---|---|---|
0 | 10101 | 50 |
0.5 | 0 | 0 |
1 | 0 | 0 |
1.5 | 10101 | -50 |
Bolus infusion with rate 50 to compartment 1 for 1.5 hr (modeled bioavailability changes rate of infusion)
time | evid | amt |
---|---|---|
0 | 20101 | 50 |
0.5 | 0 | 0 |
1 | 0 | 0 |
1.5 | 20101 | -50 |
Modeled rate with amount of 50
time | evid | amt |
---|---|---|
0 | 90101 | 50 |
0 | 70101 | 50 |
0.5 | 0 | 0 |
1 | 0 | 0 |
Modeled duration with amount of 50
time | evid | amt |
---|---|---|
0 | 80101 | 50 |
0 | 60101 | 50 |
0.5 | 0 | 0 |
1 | 0 | 0 |
7.7.3 Steady State for classic RxODE EVID example
Steady state dose to cmt 1
time | evid | amt |
---|---|---|
0 | 110 | 50 |
Steady State with super-positioning principle for am 50 and pm 100 dose
time | evid | amt |
---|---|---|
0 | 110 | 50 |
12 | 120 | 100 |
7.7.4 Turning off a compartment with classic RxODE EVID
Turn off the first compartment at time 12
time | evid | amt |
---|---|---|
0 | 110 | 50 |
12 | 130 | NA |
Event coding in RxODE is encoded in a single event number evid
. For
compartments under 100, this is coded as:
- This event is
0
for observation events. - For a specified compartment a bolus dose is defined as:
- 100*(Compartment Number) + 1
- The dose is then captured in the
amt
- For IV bolus doses the event is defined as:
- 10000 + 100*(Compartment Number) + 1
- The infusion rate is captured in the
amt
column - The infusion is turned off by subtracting
amt
with the sameevid
at the stop of the infusion.
For compartments greater or equal to 100, the 100s place and above
digits are transferred to the 100,000th place digit. For doses to the
99th compartment the evid
for a bolus dose would be 9901
and the
evid
for an infusion would be 19901
. For a bolus dose to the
199
th compartment the evid
for the bolus dose would be
109901
. An infusion dosing record for the 199
th compartment would
be 119901
.
7.8 Datasets for RxODE & nlmixr
Data for input into nlmixr
is the same type of data input for RxODE
, and it is similar to data for NONMEM (most NONMEM-ready datasets can be used directly in nlmixr
).
7.9 Columns Described by Type of Use
7.9.1 Subject Identification Columns
The subject identification column separates subjects for identification of random effects.
ID
: A subject identifier that may be an integer, character, or factor.
7.9.2 Observation Columns
Observation columns are used to indicate the dependent variable and how to use or measure it.
DV
: A numeric column with the measurementCENS
: A numeric column for indication of censoring, such as below the limit of quantification for an assay.LIMIT
: A numeric column for helping indicate the type of censoring, such as below the limit of quantification for an assay.MDV
: An indicator for missingDV
valuesCMT
: The name or number of the compartmentDVID
: The dependent variable identifierEVID
The event identifier
7.9.3 Dosing Columns
AMT
: The amount of the doseCMT
: The name or number of the compartmentEVID
: The event identifierADDL
: The number of additional dosesRATE
orDUR
: The rate or duration of a dose
7.9.4 Covariate Columns
7.10 Details for Specific Dataset Columns
The details below are sorted alphabetically by column name. For grouping by use, see the documentation above.
7.10.1 AMT
Column
The AMT
column defines the amount of a dose.
For observation rows, it should be 0
or NA
.
For dosing rows, it is the amount of the dose administered to the CMT
. If the dose has a zero-order rate (such as a constant infusion), the infusion may be setup using the RATE
or DUR
column.
7.10.2 CENS
/LIMIT
Columns
The CENS
column is an indicator column indicating if censoring occurred. For pharmacokinetic modeling, censoring is typically when a sample is below the limit of quantification. Internally RxODE
saves these values so that nlmixr
can use them in likelihood calculations.
CENS = 0
indicates that the value in DV
is measured without censoring.
CENS = 1
indicates that a value is left censored (or below the limit of quantitation) and that the value in DV
is censoring/quantitation limit.
CENS = -1
indicates that a value is right censored (or above limit of quantitation) and that the value in DV
is censoring/quantitation limit.
The LIMIT
is additional information about how censoring is handled with nlmixr
and is stored in RxODE
’s data structure as well. When a value is left censored, like below a limit of 1
you may also believe that the value is above a certain threshold, like zero. In this case, a limit of 0
indicates that the censored value is between 0
and 1
.
In short when:
CENS = 0
a LIMIT
is ignored because the observation is not censored
CENS = 1
the value is censored between (LIMIT
, DV
)
CENS = -1
the value is censored between (DV
, LIMIT
)
7.10.3 CMT
Column
The CMT
column indicates the compartment where an event occurs. When given as a character string or factor (the preferred method), it is matched by name in the model. When given as an integer, it is matched by the order that compartments appear in the model.
7.10.4 DUR
Column
The DUR
column defines the duration of an infusion. It is used to set the duration of a zero-order rate of infusion.
7.10.5 DV
Column
The DV
column indicates the current measurement in the current compartment (see CMT
) with the current measurement identifier (see DVID
) which may be missing (see MDV
) or censored (see CENS
).
7.10.6 DVID
Column
TODO
7.10.7 EVID
Column
The EVID
column is the event identifier for a row of data.
For observation records, it will be 0
. For normal dosing records, it will be 1
. Many more EVID
values are detailed in the RxODE Event Types and Classic RxODE Events vignettes.
7.10.8 ID
Column
The ID
column is a subject identifier. This column is used to separate one individual (usually a single person or animal) from another.
In the model, the ID
column is used to separate individuals. The numerical integrator re-initializes with each new individual, and new values for all random effects are selected.
7.10.9 RATE
Column
TODO