# Dosage Regimens, Subjects, and Populations

The data is passed to a Pumas model via the `Population`

type. A `Population`

is a collection of `Subject`

s. In this section we will specify the methods used for defining `Subject`

s and `Population`

s. These types can either be defined programmatically using the `Subject`

and `Population`

constructors on Julia types or by using the Pumas NLME Data format (named PumasNDF).

## Dosage Regimen Terminology

Both the `DosageRegimen`

and the `PumasNDF`

utilize the same terminology for describing a dose. The definition of the values are as follows:

`amt`

: the amount of the dose. This is the only required value.`time`

: the time at which the dose is given. Defaults to 0.`evid`

: the event id.`1`

specifies a normal event.`3`

means it's a reset event, meaning that the value of the dynamical variable is reset to the`amt`

at the dosing event. If`4`

, then the value is reset (to the steady state), and then a final dose is given. Defaults to`1`

.`ii`

: the interdose interval. For steady state events, this is the length of time between successive doses. When`addl`

is specified, this is the length of time to the next dose. Defaults to`0`

.`addl`

: the number of additional events of the same types, spaced by`ii`

. Defaults to 0.`rate`

: the rate of administration. If`0`

, then the dose is instantaneous. Otherwise the dose is administrated at a constant rate for a duration equal to`amt/rate`

.`ss`

: an indicator for whether the dose is a steady state dose. A steady state dose is defined as the result of having applied the dose with the interval`ii`

infinitely many successive times.`0`

indicates that the dose is not a steady state dose. 1 indicates that the dose is a steady state dose. 2 indicates that it is a steady state dose that is added to the previous amount. The default is 0.

## The Subject Constructor

A `Subject`

can be constructed using the following constructor:

```
Subject(;id = 1,
obs = nothing,
cvs = nothing,
evs = Event[],
time = obs isa AbstractDataFrame ? obs.time : nothing
)
```

The definitions of the arguments are as follows:

`id`

is the id of the subject. Defaults to`1`

.`obs`

is a Julia type which holds the observational data. When using the`@model`

interface, this must be a`NamedTuple`

whose names match those of the derived variables.`cvs`

are the covariates for the subject. It can be any Julia type when working with the function-based interface, but must be a`NamedTuple`

for the`@model`

interface. Defaults to`nothing`

, meaning no covariates.`evs`

is a`DosageRegimen`

. Defaults to an empty event list.`time`

is the list of times associated with the observations.

`DosageRegimen`

The `DosageRegimen`

type is a specification of a regimen. Its constructor is:

```
DosageRegimen(amt;
time = 0,
cmt = 1,
evid = 1,
ii = zero.(time),
addl = 0,
rate = zero.(amt)./oneunit.(time),
ss = 0)
```

Each of the values can either be `AbstractVector`

s or scalars. All vectors must be of the same length, and the elementwise combinations each define an event (with scalars being repeated).

Additionally, multiple `DosageRegimen`

s can be combined to form a `DosageRegimen`

. For example, if we have:

```
dr1 = DosageRegimen(100, ii = 24, addl = 6)
dr2 = DosageRegimen(50, ii = 12, addl = 13)
dr3 = DosageRegimen(200, ii = 24, addl = 2)
```

then the following is the `DosageRegimen`

for the combination of all doses:

`dr = DosageRegimen(e1, e2, e3)`

The current `DosageRegimen`

can be viewed in its tabular form using the `DataFrame`

function: `DataFrame(dr)`

.

## The Population Constructor

The `Population`

constructor is simply `Population(subjects)`

, where `subjects`

is a collection of `Subject`

s.

## PumasNDF

The PumasNDF is a specification for building a `Population`

from tabular data. Generally this tabular data is given by a database like a CSV. The CSV has columns described as follows:

`id`

: the ID of the individual. Each individual should have a unique integer, or string.`time`

: the time corresponding to the row. Should be unique per id, i.e. no duplicate time values for a given subject.`evid`

: the event id.`1`

specifies a normal event.`3`

means it's a reset event, meaning that the value of the dynamical variable is reset to the`amt`

at the dosing event. If`4`

, then the value is reset (to the steady state), and then a final dose is given. Defaults to`0`

if amt is`0`

or missing, and 1 otherwise.`amt`

: the amount of the dose. If the`evid`

column exists and is non-zero, this value should be non-zero. Defaults to`0`

.`ii`

: the interdose interval. When`addl`

is specified, this is the length of time to the next dose. For steady state events, this is the length of time between successive doses. Defaults to`0`

, and is required to be non-zero on rows where a steady-state event is specified.`addl`

: the number of additional doses of the same time to give. Defaults to 0.`rate`

: the rate of administration. If`0`

, then the dose is instantaneous. Otherwise the dose is administrated at a constant rate for a duration equal to`amt/rate`

. A`rate=-2`

allows the`rate`

to be determined by a Dosing Control Parameters (DCP). Defaults to`0`

.`ss`

: an indicator for whether the dose is a steady state dose. A steady state dose is defined as the result of having applied the dose with the interval`ii`

infinitely many successive times.`0`

indicates that the dose is not a steady state dose.`1`

indicates that the dose is a steady state dose.`2`

indicates that it is a steady state dose that is added to the previous amount. The default is`0`

.`cmt`

: the compartment being dosed. Defaults to`1`

.`duration`

: the duration of administration. If`0`

, then the dose is instantaneous. Otherwise the dose is administered at a constant rate equal to`amt/duration`

. Defaults to`0`

.- Observation and covariate columns should be given as a time series of values of matching type. Constant covariates should be constant through the full column. Time points without a measurement should be denoted by a
`.`

.

If a column does not exists, its values are imputed to be the defaults. Special notes:

- If
`rate`

and`duration`

exist, then it is enforced that`amt=rate*duration`

- All values and header names are interpreted as lower case.

### PumasNDF Parsing

```
read_Pumas(data;vs=Symbol[],dvs=Symbol[:dv]
id=:id, time=:time, evid=:evid, amt=:amt, addl=:addl,
ii=:ii, cmt=:cmt, rate=:rate, ss=:ss)
```

The arguments are as follows:

`data`

is the tabular data source. If a string path to a CSV is given, then it will read the CSV file from that location in the file system. If a Julia-based tabular data structure is given, such as a DataFrame, then that structure will be used as the data source.`cvs`

is the list of symbols for the header names of the covariate columns.`dvs`

is the list of symbols for the header names of the observation data columns.

The other arguments are optional and allow changing the column names from their default.

### NMTRAN Parsing

Additionally, there exist a parsing function `process_nmtran`

for parsing general NONMEM files. This function is a work-in-progress.

```
process_nmtran(data,cvs=Symbol[],dvs=Symbol[:dv];
id=:id, time=:time, evid=:evid, amt=:amt, addl=:addl,
ii=:ii, cmt=:cmt, rate=:rate, ss=:ss)
```