causalprotect 0.1.1

Implementation of the PROTECT method for proxy-based causal inference

PROTECT: PRoxy-based individual Treatment EffeCT modeling in cancer

Official implementation of PROTECT

installation

Development version: clone this repository, run

pip install -e .

To be able to run the tutorial notebook, also install the dev dependencies:

pip install -e .[dev]

Usage

For applying PROTECT to your own data, see the notebook tutorials/inference_demo.ipynb To run this, you'll need to install the dev dependencies You'll need to specify these files (as seen in the tutorial directory)

• model.py you're own bespoke PROTECT model variant
• metadata.yaml some background information on the model and potential flags to control the model computation
• priors.csv a csv file that specifies the priors for the model (can also be provided in other formats, see protect/models.py for details)

data formatting

Obligatory variables are

• tx a binary treatment indicator
• time_cens: a floating point variable with time_cens = time for patients who had the event, and time_cens = -time for patients who were censored, where:

time_cens = (2*event - 1) * time

You can create this from a time and event variable using protect.utils.time_event_to_time_cens(time,event)