The pygformula package implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm [1] , [2]. The g-formula can estimate an outcome’s counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders.
This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments.
For a quick overview of how to use the pygformula, see a simple example in :doc:`Get Started`. For a detailed list of options, see :doc:`Specifications/index`.
.. toctree:: :maxdepth: 2 Installation Get Started
.. toctree:: :maxdepth: 4 Specifications/index
.. toctree:: :maxdepth: 2 Datasets Contact
| [1] | Robins JM. A new approach to causal inference in mortality studies with a sustained exposure period: application to the healthy worker survivor effect. Mathematical Modelling. 1986;7:1393–1512. [Errata (1987) in Computers and Mathematics with Applications 14, 917-921. Addendum (1987) in Computers and Mathematics with Applications 14, 923-945. Errata (1987) to addendum in Computers and Mathematics with Applications 18, 477. |
| [2] | Hernán, M.A., and Robins, J. (2020). Causal Inference: What If (Chapman & Hall/CRC). |