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edamame

For decision analytic modeling and cost-effectiveness analysis

https://github.com/brandoncchan/edamame

Science Score: 44.0%

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Repository

For decision analytic modeling and cost-effectiveness analysis

Basic Info
  • Host: GitHub
  • Owner: BrandonCChan
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 7.47 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 5 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

edamame

Economic Decision Analytic MArkov Model Evaluation (EDAMAME)

A framework and tools for decision analytic modeling and cost-effectiveness analysis written in Python. Mainly intended for use in the field of health economics to conduct economic evaluations using mote-carlo markov model simulations. Allows users to flexibly define model schematics and parameters in excel workbooks before utilizing edamame code to load, run, and generate model outputs.

Requirements:

See requirements.txt for more details

Reflects development environment but is possible that older and newer versions of packages may still work.

Running a cost-effectivness analysis

See Cost-Effectivness Analysis.ipynb for an end-to-end example with annotations and explainations!

Alternativley, costeffectivnessanalysis.py provides a script version of most of the code within the example jupyter notebook.

Defining and specifying a model

The structure (i.e. states and transitions), associated state costs, associated state utilties, and simulation paramters (i.e. number of iterations, time-horizon) of a model are defined as a formatted excel document. Each model arm (comparator) is defined by it's own excel workbook. Templates of a simple 3 state model are provided in here.

See the wiki page for more details.

Other resources and examples

Model disgnostics and debugging

Model Diagnostics.ipynb contains two means of qualitativley checking models. First is the ability to visualize the model specified in an excel file as a graph. Second is to track and visualize the movement of the "population" throughout each model cycle.

Univariate sensitivity analysis

Univariate Sensitivity Analysis.ipynb provides an example of conducting two types of univariate sensitivity analysis using previously saved model outputs. 1) Is the adjustment of the cost of a state; 2) Is the adjustment of a transition probability.

Example of time-varying transition probabilities

Testing with two-state model demonstrates the use of time-varying transition probabilities (i.e. a weibull) with a simple 2-state model. This example is useful in testing model behavior and validating the parameterization of a survivial regression you may have run with external data.

Documentation

See the relevant wiki pages

Owner

  • Login: BrandonCChan
  • Kind: user

Queen's University MSc '19 | Queen's University Biomedical Computing '17

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Chan"
  given-names: "Brandon"
  orcid: "https://orcid.org/0000-0003-1923-0052"
title: "edamame"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2022-06-28
url: "https://github.com/BrandonCChan/edamame"

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Dependencies

requirements.txt pypi
  • Brotli ==1.0.9
  • Cython ==0.29.21
  • Flask ==1.1.2
  • Flask-Compress ==1.8.0
  • Jinja2 ==2.11.2
  • MarkupSafe ==1.1.1
  • Pillow ==8.1.0
  • Pygments ==2.7.4
  • QtPy ==1.9.0
  • Send2Trash ==1.5.0
  • Werkzeug ==1.0.1
  • ansi2html ==1.6.0
  • arch ==4.15
  • argon2-cffi ==20.1.0
  • async-generator ==1.10
  • attrs ==20.3.0
  • backcall ==0.2.0
  • bleach ==3.2.3
  • certifi ==2020.12.5
  • cffi ==1.14.4
  • chardet ==4.0.0
  • click ==7.1.2
  • colorama ==0.4.4
  • cycler ==0.10.0
  • dash ==1.19.0
  • dash-core-components ==1.15.0
  • dash-cytoscape ==0.2.0
  • dash-html-components ==1.1.2
  • dash-renderer ==1.9.0
  • dash-table ==4.11.2
  • decorator ==4.4.2
  • defusedxml ==0.6.0
  • entrypoints ==0.3
  • et-xmlfile ==1.0.1
  • future ==0.18.2
  • idna ==2.10
  • importlib-metadata ==3.4.0
  • ipykernel ==5.4.3
  • ipython ==7.19.0
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.6.3
  • itsdangerous ==1.1.0
  • jdcal ==1.4.1
  • jedi ==0.18.0
  • jsonschema ==3.2.0
  • jupyter ==1.0.0
  • jupyter-client ==6.1.11
  • jupyter-console ==6.2.0
  • jupyter-core ==4.7.0
  • jupyter-dash ==0.4.0
  • jupyterlab-pygments ==0.1.2
  • jupyterlab-widgets ==1.0.0
  • kiwisolver ==1.3.1
  • matplotlib ==3.3.3
  • mistune ==0.8.4
  • nbclient ==0.5.1
  • nbconvert ==6.0.7
  • nbformat ==5.1.2
  • nest-asyncio ==1.4.3
  • networkx ==2.5
  • notebook ==6.2.0
  • numpy ==1.19.5
  • openpyxl ==3.0.6
  • packaging ==20.8
  • pandas ==1.2.1
  • pandocfilters ==1.4.3
  • parso ==0.8.1
  • patsy ==0.5.1
  • pickleshare ==0.7.5
  • plotly ==4.14.3
  • prometheus-client ==0.9.0
  • prompt-toolkit ==3.0.14
  • property-cached ==1.6.4
  • pycparser ==2.20
  • pyparsing ==2.4.7
  • pyrsistent ==0.17.3
  • python-dateutil ==2.8.1
  • pytz ==2020.5
  • pywin32 ==300
  • pywinpty ==0.5.7
  • pyzmq ==21.0.2
  • qtconsole ==5.0.2
  • requests ==2.25.1
  • retrying ==1.3.3
  • scipy ==1.6.0
  • seaborn ==0.11.1
  • six ==1.15.0
  • statsmodels ==0.12.1
  • terminado ==0.9.2
  • testpath ==0.4.4
  • tornado ==6.1
  • traitlets ==5.0.5
  • typing-extensions ==3.7.4.3
  • urllib3 ==1.26.2
  • wcwidth ==0.2.5
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.1
  • wincertstore ==0.2
  • zipp ==3.4.0