Covariate Model

This example demonstrates a two-compartment oral model with body weight (WT) and creatinine clearance (CRCL) as covariates on clearance.

Model File (`two_cpt_oral_cov.ferx`)

# Two-compartment oral PK model with covariates

[parameters]
  theta TVCL(5.0, 0.01, 100.0)
  theta TVV1(50.0, 0.1, 1000.0)
  theta TVQ(10.0, 0.01, 200.0)
  theta TVV2(100.0, 0.1, 5000.0)
  theta TVKA(1.0, 0.01, 50.0)
  theta THETA_WT(0.75, 0.01, 2.0)
  theta THETA_CRCL(0.5, 0.01, 2.0)

  omega ETA_CL ~ 0.09
  omega ETA_V1 ~ 0.04
  omega ETA_Q  ~ 0.04
  omega ETA_V2 ~ 0.09
  omega ETA_KA ~ 0.25

  sigma PROP_ERR ~ 0.04

[individual_parameters]
  CL = TVCL * (WT/70)^THETA_WT * (CRCL/100)^THETA_CRCL * exp(ETA_CL)
  V1 = TVV1 * exp(ETA_V1)
  Q  = TVQ  * exp(ETA_Q)
  V2 = TVV2 * exp(ETA_V2)
  KA = TVKA * exp(ETA_KA)

[structural_model]
  pk two_cpt_oral(cl=CL, v1=V1, q=Q, v2=V2, ka=KA)

[error_model]
  DV ~ proportional(PROP_ERR)

[fit_options]
  method     = focei
  maxiter    = 500
  covariance = true

Covariate Effects

The clearance equation includes two covariate effects:

CL = TVCL * (WT/70)^THETA_WT * (CRCL/100)^THETA_CRCL * exp(ETA_CL)

(WT/70)^THETA_WT: Allometric scaling of clearance with body weight, centered at 70 kg. THETA_WT is estimated (expected ~0.75 for CL).
(CRCL/100)^THETA_CRCL: Renal function effect on clearance, centered at 100 mL/min. THETA_CRCL is estimated.

Data Requirements

The dataset must include WT and CRCL columns:

ID,TIME,DV,EVID,AMT,CMT,MDV,WT,CRCL
1,0,.,1,100,1,1,72.5,105
1,0.5,12.3,0,.,.,0,72.5,105
1,1.0,18.7,0,.,.,0,72.5,105

Covariate columns are automatically detected -- any column not in the standard NONMEM set is treated as a covariate. The names in the data file must match those used in [individual_parameters] (case-insensitive).

Running

ferx examples/two_cpt_oral_cov.ferx --data data/two_cpt_oral_cov.csv

Notes

FOCEI is used here because the proportional error model creates an interaction between random effects and residual error
Covariate centering (dividing by 70 for weight, 100 for CRCL) improves numerical stability and makes the typical value (TVCL) interpretable as the clearance for a 70 kg patient with CRCL of 100 mL/min
The estimated covariate exponents (THETA_WT, THETA_CRCL) have standard errors that can be used to test significance

FeRx-NLME Documentation