set_direct_effect#

pharmpy.modeling.set_direct_effect(model, expr, variable=None)[source]#

Add an effect to a model.

Effects are by default using concentratrion, but any user specified variable in the model can be used. Implemented PD models are:

• Linear:

  $$E=B\cdot (1+\text{slope}\cdot C)$$

• Emax:

  $$E=B\cdot (1+\frac{E_{max}\cdot C}{E{C}_{50}+C})$$

• Step effect:

  $$E=\{\genfrac{}{}{0ex}{}{B\text{if C}\le 0}{B\cdot (1+E_{max})\text{else}}$$

• Sigmoidal:

  $$E=\{\genfrac{}{}{0ex}{}{B\cdot (1+\frac{E_{max}\cdot C^n}{E{C}_{50}^n+C^n})\text{if C}>0}{B\text{else}}$$

• Log-linear:

  $$E=\text{slope}\cdot \text{log}(C+C_0)$$

$B$ is the baseline effect

Parameters:

• model (Model) – Pharmpy model

• expr ({‘linear’, ‘emax’, ‘sigmoid’, ‘step’, ‘loglin’}) – Name of PD effect function.

• variable (str) – Name of variable to use (if None concentration will be used)

Returns:

Model – Pharmpy model object

Examples

>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = set_direct_effect(model, "linear")
>>> model.statements.find_assignment("E")
      ⎛SLOPE⋅A_CENTRAL(t)    ⎞
    B⋅⎜────────────────── + 1⎟
E =   ⎝        VC            ⎠