add_covariate_effect#

pharmpy.modeling.add_covariate_effect(model, parameter, covariate, effect, operation='*', allow_nested=False)[source]#

Adds covariate effect to pharmpy.model.

The following effects have templates:

• Linear function for continuous covariates (lin)

  • Function:

  $$\text{coveff}=1+\text{theta}\ast (\text{cov}-\text{median})$$

  • Init: 0.001

  • Upper:

    • If median of covariate equals minimum: 100,000

    • Otherwise: $\frac{1}{\text{median}-\text{min}}$

  • Lower:

    • If median of covariate equals maximum: -100,000

    • Otherwise: $\frac{1}{\text{median}-\text{max}}$

• Linear function for categorical covariates (cat)

  • Function:

    • If covariate is the most common category:

    $$\text{coveff}=1$$

    • For each additional category:

    $$\text{coveff}=1+\text{theta}$$

  • Init: 0.001

  • Upper: 5

  • Lower: -1

• (alternative) Linear function for categorical covariates (cat2)

  • Function:

    • If covariate is the most common category:

    $$\text{coveff}=1$$

    • For each additional category:

    $$\text{coveff}=\text{theta}$$

  • Init: 0.001

  • Upper: 6

  • Lower: 0

• Piecewise linear function/”hockey-stick”, continuous covariates only (piece_lin)

  • Function:

    • If cov <= median:

    $$\text{coveff}=1+\text{theta1}\ast (\text{cov}-\text{median})$$

    • If cov > median:

    $$\text{coveff}=1+\text{theta2}\ast (\text{cov}-\text{median})$$

  • Init: 0.001

  • Upper:

    • For first state: $\frac{1}{\text{median}-\text{min}}$

    • Otherwise: 100,000

  • Lower:

    • For first state: -100,000

    • Otherwise: $\frac{1}{\text{median}-\text{max}}$

• Exponential function, continuous covariates only (exp)

  • Function:

  $$\text{coveff}=\exp (\text{theta}\ast (\text{cov}-\text{median}))$$

  • Init:

    • If lower > 0.001 or upper < 0.001: $\frac{\text{upper}-\text{lower}}{2}$

    • If estimated init is 0: $\frac{\text{upper}}{2}$

    • Otherwise: 0.001

  • Upper:

    • If min - median = 0 or max - median = 0: 100

    • Otherwise:

    $$min(\frac{\log (0.01)}{\text{min}-\text{median}},\frac{\log (100)}{\text{max}-\text{median}})$$

  • Lower:

    • If min - median = 0 or max - median = 0: 0.01

    • Otherwise:

    $$max(\frac{\log (0.01)}{\text{max}-\text{median}},\frac{\log (100)}{\text{min}-\text{median}})$$

• Power function, continuous covariates only (pow)

  • Function:

  $$\text{coveff}=(\frac{\text{cov}}{\text{median}}{)}^{\text{theta}}$$

  • Init: 0.001

  • Upper: 100,000

  • Lower: -100

Parameters:

• model (Model) – Pharmpy model to add covariate effect to.

• parameter (str) – Name of parameter to add covariate effect to.

• covariate (str) – Name of covariate.

• effect (str) – Type of covariate effect. May be abbreviated covariate effect (see above) or custom.

• operation (str, optional) – Whether the covariate effect should be added or multiplied (default).

• allow_nested (bool, optional) – Whether to allow adding a covariate effect when one already exists for the input parameter-covariate pair.

Returns:

Model – Pharmpy model object

Examples

>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = add_covariate_effect(model, "CL", "APGR", "exp")
>>> model.statements.before_odes.full_expression("CL")
POP_CL*WGT*exp(ETA_CL + POP_CLAPGR*(APGR - 7.0))