## Introduction

TAD stands for time after dose. In mrgsolve, any time TAD is calculated automatically, a dose is a record in the input data set where EVID is set to either 1 or 4.

## TAD calculated as runtime argument

For a while, you’ve been able to get time after dose in your simulated output

library(mrgsolve)
library(dplyr)

mod <- modlib("pk1", req = "")

mod %>%
ev(amt = 100, ii = 4, addl = 3, time = 2) %>%
as_tibble() %>%
head(n = 10)
.  [38;5;246m# A tibble: 10 × 4 [39m
.     [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m
.  [38;5;250m 1 [39m     1  0    - [31m2 [39m     0
.  [38;5;250m 2 [39m     1  1    - [31m1 [39m     0
.  [38;5;250m 3 [39m     1  2     0     0
.  [38;5;250m 4 [39m     1  2     0     0
.  [38;5;250m 5 [39m     1  3     1     3.07
.  [38;5;250m 6 [39m     1  3.89  1.89  3.99
.  [38;5;250m 7 [39m     1  4     2     4.05
.  [38;5;250m 8 [39m     1  5     3     4.27
.  [38;5;250m 9 [39m     1  5.91  3.91  4.22
.  [38;5;250m10 [39m     1  6     0     4.21

This is convenient because you can choose the output at run time, we give you the negative numbers prior to the first dose etc. This sort of calculation is possible because we let mrgsolve know ahead of time that we want this calculation done and mrgsolve makes an extra pass through the records to find when is the first dose for each individual.

## TAD calculated in the model

Sometimes you would like to work with time after dose in your model. This isn’t super-complicated to do but does require some programming and setup and all of that. As of mrgsolve 0.9.1, there is a special function to do these calculations for you.

mod <- mread("time-after-dose.txt",req = "")

Looking at the [MAIN] block:

When you call self.tad() you will get the time after dose. It’s important that this gets called on every record … specifically every dosing record. It will not work properly if it is not called every record and there is no check at this time to make sure you follow that rule. So please follow the rule.

To see an example:

[ param ] CL = 1, V = 20, KA = 1

[ pkmodel ] cmt = "GUT,CENT", depot = TRUE

[ main ]

[ table]
capture CP  = CENT/V;
mod %>%
ev(amt = 100, ii = 4, addl = 3, time = 2) %>%
as_tibble() %>%
head(n = 10)
.  [38;5;246m# A tibble: 10 × 5 [39m
.     [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m   [3m [38;5;246m<dbl> [39m [23m  [3m [38;5;246m<dbl> [39m [23m
.  [38;5;250m 1 [39m     1  0    - [31m2 [39m     - [31m1 [39m     0
.  [38;5;250m 2 [39m     1  1    - [31m1 [39m     - [31m1 [39m     0
.  [38;5;250m 3 [39m     1  2     0     - [31m1 [39m     0
.  [38;5;250m 4 [39m     1  2     0      0     0
.  [38;5;250m 5 [39m     1  3     1      1     3.07
.  [38;5;250m 6 [39m     1  3.89  1.89   1.89  3.99
.  [38;5;250m 7 [39m     1  4     2      2     4.05
.  [38;5;250m 8 [39m     1  5     3      3     4.27
.  [38;5;250m 9 [39m     1  5.91  3.91   3.91  4.22
.  [38;5;250m10 [39m     1  6     0      0     4.21

You will notice two differences between tad (output requested at run time) and tadose (values calculated in the model itself) in the output listing above:

1. tadose is -1 before the first dose
2. specifically, tadose is -1 at the 2 hour observation record that occurs that the same time as the dose, but happens before the dose in record order

The main point if this is that you can easily obtain time after dose in the problem (model) itself to use as you program the model and also output the number into the simulated output.

## Time after dose in specific compartment

Version 0.10.7 adds a new plugin with the ability to calculate time after dose in any compartment.

We write a model using the tad plugin to track time after dose in compartments one and two. We create tadose objects to track this and we can call the tad() method on these objects, passing in the self data item.

code <- '

[ global ]

[ pkmodel ] cmt = "GUT,CENT", depot = TRUE

[ param ] CL = 1, V = 20, KA = 1

[ main ]

'

mod <- mcode("tad", code, soloc = '.')

data <- c(
ev(amt = 100, cmt = 1, time = 1),
ev(amt = 200, cmt = 2, time = 3)
)

mrgsim(mod, data)
. Model:  tad
. Dim:    27 x 6
. Time:   0 to 24
. ID:     1
. 1:   1    0   0.000   0.00   -1   -1
. 2:   1    1   0.000   0.00   -1   -1
. 3:   1    1 100.000   0.00    0   -1
. 4:   1    2  36.788  61.41    1   -1
. 5:   1    3  13.534  81.00    2   -1
. 6:   1    3  13.534 281.00    2    0
. 7:   1    4   4.979 275.61    3    1
. 8:   1    5   1.832 265.22    4    2

Note that time after dose is -1 until a dose is administered.

Recall also that time after dose can be calculated more simply if there is only one dose type by passing the tad argument:

data1 <- filter(data, cmt ==1) %>% mutate(time = 3)
mod %>% mrgsim(data1, tad = TRUE)
. Model:  tad
. Dim:    26 x 7
. Time:   0 to 24
. ID:     1
. 8:   1    6   3   4.979 85.36    3   -1
This is a little nicer because it will fill in negative tad values for you.