call takes individual and pop parameters and returns score
pools_parameters return False or True
Implement a PoolParameters class that simply takes all parameters and sets them equal.
Evaluation returns 0
sampling return the input parameter
HierarchicalLogLikelihood checks whether pop pools, if so, adds just n_pop_params to input parameters, if not adds individual params and n_pop_params.
Then distributes the right parameters to the right log_likelihoods and PopulationLogPDFs. Possibly index entries for each individual loglikelihood and also index(mask) parameters for the population parameters.
Implement support in the ProblemModellingController
Create a SimulationController dash app with the following features
Initialises as an empty figure.
Has an add model method that sets the model to be simulated (for now overwrite any previous model)
Generates sliders for all parameters of the model
Solves the model live for slider values (for now only allow SingleOutput models)
Has an add data method that shows the creates a scatter representation of the data
Has a method to set the simulation time range
Has a method that starts the app.
Potentially: Has a method that returns a static plotly.Figure of some slider configuration, such that simulation may be presented with nbviewer.
Possible extensions:
Allow multiple outputs
Allow simulation of multiple models
Allow marking of current simulation, i.e. create a permanent trace of the current simulation, so outputs of multiple simulations may be more easily compared.
pandas.DataFrame with keys for ID, time, and at least one biomarker. Optionally a dose key can be provided, by default None
Set model method accepts PharmacodynamicModels and PharmacokineticModels.
If dose_key None PharmacokineticModel is provided, show a Warning that doses are not set from the data.
If dose_key is provided and PharmacodynamicModel is provided, show a Warning that for PharmacodynamicModels dosing regimens cannot be set
If dose_key is not None and PharmacokineticModel is provided, set doses according to dataframe when error models are create. I.e. set flag self._dose_applied to True.
When this is done, complete #56 by 'hacking', i.e. set ID in dataset to pooled for all individuals and run optimisation.
In this issue I am creating a notebook that takes the lung cancer low dose data from [1], imports it, cleans it, and exports the cleaned data file
Put data in raw_data repository
Import data
Interpret columns
Clean data
Visualise data
Export data to data directory
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
Create a notebook that solves the tumour growth PD model with the error model suggested by Eigenmann et al.
Solve PD model in absence of treatment
Create sliders for all mechanistic and error parameters. Because combining parameters arbitrarily when precomputing the model, it makes sense to find the optimal parameters for one individual, and allow the sliders to perturb values around this optimal solution.
Create a PDModel class that inherits from the base class. This class should define methods that specify the PK input / and PD output states.
Create a PKModel class that inherits from the base class. This class should define methods that specify the PD input / and PK output states.
Create a PKPDModel class that inherits from the base class. This model takes a PK and PD model and combines them based on their specified PK/PD input/output states.
Improve docs of set administration (align equal signs)
In this issue I am creating a notebook that takes the lung cancer control growth data from [1], imports it, cleans it, and exports the cleaned data file
Put data in raw_data repository
Import data
Interpret columns
Clean data
Visualise data
Export data to data directory
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
In this issue I am creating a notebook that takes the lung cancer control growth data from [1], imports it, cleans it, and exports the cleaned data file
Put data in raw_data repository
Import data
Interpret columns
Clean data
Visualise data
Export data to data directory
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
In this issue I am creating a notebook that takes the lung cancer medium dose data from [1], imports it, cleans it, and exports the cleaned data file
Put data in raw_data repository
Import data
Interpret columns
Clean data
Visualise data
Export data to data directory
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)