PK dataset standards: CDISC, NONMEM data items, and the defects that survive review

The two worlds

Regulatory submission data is CDISC. Modelling data is NONMEM-format. They are different shapes and converting between them is where most defects are introduced.

Layer	Domain / dataset	Contents
SDTM	PC	Pharmacokinetic concentrations, as collected
SDTM	PP	Pharmacokinetic parameters (NCA output)
SDTM	EX	Exposure — what was actually administered
ADaM	ADPC	Analysis-ready concentrations
ADaM	ADPP	Analysis-ready parameters
—	NONMEM dataset	One row per event, wide covariates, numeric only

Useful SDTM PC variables: PCTESTCD/PCTEST (analyte), PCORRES/PCSTRESN (result as collected and standardised), PCSTRESU, PCLLOQ, PCTPT/PCTPTNUM (nominal time), PCDTC (actual date/time), PCSPEC (matrix). PP parameters use the CDISC PKPARM/PKUNIT controlled terminology — AUCALL, AUCIFO, AUCIFP, CMAX, TMAX, LAMZ, LAMZHL, CLFO, VZFO.

Nominal versus actual time is the single most consequential conversion decision. NCA and population modelling should use actual elapsed time from the most recent dose. Using nominal time flattens the absorption phase, biases Cmax and Tmax, and inflates residual error. Nominal time is for grouping and presentation only.

NONMEM data items

Item	Meaning	Traps
`ID`	Subject	Must be numeric and contiguous per subject; records for one subject must be together
`TIME`	Elapsed time	Must be non-decreasing within a subject. Use one unit consistently
`DV`	Dependent variable	Must be numeric. See below
`AMT`	Dose amount	On a dose record only; units must match the model's
`EVID`	Event ID	0 observation, 1 dose, 2 other, 3 reset, 4 reset+dose
`MDV`	Missing DV	1 means the record contributes nothing to the objective function
`CMT`	Compartment	Which compartment is dosed or observed
`RATE`	Infusion rate	`>0` a rate; `-1` model-estimated duration; `-2` model-estimated rate
`SS`	Steady state	1 = achieve steady state before this dose; requires `II`
`II`	Interdose interval	Required by both `SS` and `ADDL`
`ADDL`	Additional doses	`n` further doses every `II`; silently does nothing without `II`

The defects that do not stop a run

These are the reason check_popk_dataset.py exists. None of them raises an error in NM-TRAN.

Non-numeric DV. BLQ, <LLOQ, ND are read as 0 and fitted as genuine zero concentrations. This is the most damaging defect in the list, and it is invisible.
Missing covariate read as 0. A blank or . in a WT column becomes a 0 kg patient in the covariate model. Missing covariates must be imputed explicitly and the imputation documented, or the subject excluded.
ADDL without II. No additional doses are placed. Exposure is understated by the whole accumulation.
SS without II. Same class of failure.
Duplicate timestamps. A dose and an observation at the same TIME are applied in file order, so whether the sample is pre- or post-dose depends on row order. Order dose records before observations at the same time, or offset the observation by a small negative amount.
Unsorted TIME within a subject. NONMEM does not sort for you.
A subject with doses but no observations. They contribute no information but appear in the N of the analysis and their etas come entirely from the prior.
A subject with observations but no dose. Their predictions are zero and their residuals are the whole observation.
Time-varying covariate declared as baseline. The model uses whichever value is on the record being evaluated, which is rarely what was intended.
Units. Dose in mg with concentrations in ng/mL gives a volume off by 10⁶. Nothing checks this; the fit will converge on a nonsense volume.
RATE left on an oral record, turning first-order absorption into a zero-order infusion.
Mixed time origins — some subjects timed from first dose, others from screening.

Handling BLQ properly

Keep the numeric DV and add a separate flag:

ID,TIME,DV,AMT,EVID,MDV,BLQ,LLOQ
1,0,.,100,1,1,0,0.5
1,1,4.21,.,0,0,0,0.5
1,24,0.5,.,0,0,1,0.5     <- DV set to LLOQ, BLQ flag set, method chosen in the control stream

Then implement the chosen method (usually M3) in the model rather than by editing the data. See population-pk.md for the M1-M7 comparison.

Structuring covariates

Baseline covariates appear once per subject and are repeated on every record.
Time-varying covariates change between records and must be declared as such. Last-observation carried forward is the usual interpolation, and it is an assumption worth stating.
Categorical covariates need a numeric coding and a documented reference level. Never leave a category blank to mean "reference".
Derived covariates (creatinine clearance, BSA, lean body weight) should be computed once, documented with the formula used, and stored — not recomputed in the control stream where the formula is invisible to a reviewer.

Dataset specification

Every population analysis dataset should ship with a specification listing, per column: name, label, type, units, derivation (including the source SDTM/ADaM variable), permissible values, and the missing-data rule. This is the document a reviewer reads first, and producing it usually surfaces at least one defect on its own.

A reproducible derivation script from the ADaM datasets to the modelling dataset is worth more than the dataset itself: it is what makes a re-run possible after a database lock update.