TYPE¶

The ‘TYPE’ field specifies the event that is happening in each row of the data file. The different types of row are as follows:-

• obs - Measurements that contribute to the log likelihood as defined in the PREDICTIONS section.
• dose - Creates a dose according to the dosing functions in the DERIVATIVES section.
• pred - Extra prediction data points. PoPy will output extra p[X] data at these time points, but they do not contribute to the likelihood.
• reset - Set the s[X] compartment states back to the initial values (usually zero)
• reset+dose - A ‘reset’ combined with a ‘dose’ event.

Typically a drug trial data set mainly consists mainly of ‘obs’ and ‘dose’ rows with a few ‘reset’ rows, per subject.

ID¶

The ‘ID’ field value defines the individual for a given row. As PoPy is a PopPK/PD system. The ‘ID’ field is required because the data is split over multiple individuals to form a population.

Note that non-population analysis can be performed in PoPy by assigning all rows the same ‘ID’ value.

TIME¶

The ‘TIME’ field defines the time stamp for each row.

The time field is required to be monotonically increasing, unless a TYPE = ‘reset’ or ‘reset+dose’ row is reached. Note that when the ID identifier changes between rows, then an implicit ‘reset’ occurs.

For an example of a valid combination of TYPE/ID/TIME data see Table 6.

In Table 6 the time always increases or stays the same in consecutive rows, but time is allowed to go backwards after a new ID or a reset.

Single Dose Type¶

The simplest way to create doses at a set of fixed times is shown in Table 7.

Note that this creates 3 doses at times [1.0, 2.0, 3.0]. The script file loading this data set should have a DERIVATIVES section something like:-

DERIVATIVES: |
    d[DEPOT] = @bolus{amt: c[AMT]} - m[KE] * s[DEPOT]

Note that the @bolus dose has no name associated with it.

Multiple Dose Types¶

If you have multiple types of dose in your analysis, e.g. two different drugs being prescribed, then you need to give each dose type a name, as shown in Table 8.

The data file above creates 2 doses of drug1 and 1 dose of drug2. The script file loading this data set should have a DERIVATIVES section something like:-

DERIVATIVES: |
    dose[drug1] = @bolus{amt: c[AMT_DRUG1]}
    dose[drug2] = @bolus{amt: c[AMT_DRUG2]}
    d[DEPOT1] = dose[drug1] - m[KE1] * s[DEPOT1]
    d[DEPOT2] = dose[drug2] - m[KE2] * s[DEPOT2]

The important aspect here is that the @bolus doses are defined with names ‘drug1’ and ‘drug2’. These names also appear in the TYPE field in the data set as ‘dose:drug1’ and ‘dose:drug2’.

An alternative naming syntax is as follows:-

DERIVATIVES: |
    d[DEPOT1] = @bolus{amt: c[AMT_DRUG1], name: 'drug1'} - m[KE1] * s[DEPOT1]
    d[DEPOT2] = @bolus{amt: c[AMT_DRUG2], name: 'drug2'} - m[KE2] * s[DEPOT2]

Note that when creating a PoPy data set, you only need to specify a name for each type of dose. You can leave the modelling decision of where each dose appears in the compartment model to a later time.

Single Observed Field¶

An example of a single observed field is shown in Table 9.

In this simple case the PREDICTIONS section may look something like:-

PREDICTIONS: |
    p[DRUG_CONC] = s[CEN]/m[V]
    c[DRUG_CONC] ~ norm(p[DRUG_CONC], m[ANOISE_var])

Note that the c[DRUG_CONC] references the ‘DRUG_CONC’ field of the data set. Here the likelihood is computed by comparing the model prediction p[DRUG_CONC] and the data file observation c[DRUG_CONC] for all rows of the data set, where TYPE = ‘obs’.

Therefore all values of the data column ‘DRUG_CONC’ have to be valid observations. If you have missing values then you need to use the data structure in Observed Field with missing data.

Observed Field with missing data¶

An example of a single observed field, with some missing data is shown in Table 10.

In this case the PREDICTIONS section may still look something like:-

PREDICTIONS: |
    p[DRUG_CONC] = s[CEN]/m[V]
    c[DRUG_CONC] ~ norm(p[DRUG_CONC], m[ANOISE_var])

However not all the TYPE = ‘obs’ rows contribute to the likelihood in this case. Only the rows that have TYPE = ‘obs’ and DRUG_CONC_FLAG = 1.

It is similar to having the following ‘if’ statement in your PREDICTIONS section:-

PREDICTIONS: |
    p[DRUG_CONC] = s[CEN]/m[V]
    if c[DRUG_CONC_FLAG] > 0.5:
        c[DRUG_CONC] ~ norm(p[DRUG_CONC], m[ANOISE_var])

You can include the ‘if’ statement in your PREDICTIONS section if you like, but it is not required (or encouraged).

Note also that missing out the ‘DRUG_CONC_FLAG’ field from your data set, has a similar effect to creating a ‘DRUG_CONC_FLAG’ field and setting all the values to 1. i.e. Flags default to 1 in PoPy.

If you have multiple observation types in your data set then flag fields become more important, see the example data structure in Multiple Observed Fields.

Multiple Observed Fields¶

An example of multiple observed fields, is shown in Table 11.

In this case the PREDICTIONS section may look something like:-

PREDICTIONS: |
    p[DRUG1] = s[CEN1]/m[V1]
    c[DRUG1] ~ norm(p[DRUG1], m[ANOISE_var1])
    p[DRUG2] = s[CEN2]/m[V2]
    c[DRUG2] ~ norm(p[DRUG2], m[ANOISE_var2])

Here PoPy uses the ‘DRUG1_FLAG’ and ‘DRUG2_FLAG’ fields from the data set to only compute the likelihood from valid observations. You don’t have to use ‘if’ statements in the PREDICTIONS section to achieve this.