Welcome to pygformula’s documentation!

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 Get Started. For a detailed list of options, see Specifications.

• Installation
  • Requirements
  • Install pygformula
• Get Started
  • Algorithm outline
  • Example

• Specifications
  • Input data
  • Interventions
    • Natural course intervention
    • Static interventions
      • static()
    • Dynamic interventions
      • natural_grace_period()
      • uniform_grace_period()
    • Threshold interventions
      • threshold()
  • Covariate models
    • Functions of covariate histories
      • Pre-coded histories
      • Custom histories
    • Different covariate distributions
      • Binary
      • Normal
      • Categorical
      • Bounded normal
      • Zero-inflated normal
      • Truncated normal
      • Absorbing
      • Time variable
      • Custom
  • Outcome model
    • Survival outcome
    • Binary end of follow-up outcome
    • Continuous end of follow-up outcome
  • Censoring event
  • Competing event
  • Hazard ratio
  • Visit process
  • Deterministic knowledge
    • Restrictions on covariates
    • Restrictions on outcome
    • Restrictions on competing event
  • Output
    • Numerical results
    • Graphical results
      • plot_natural_course()
      • plot_interventions()

• 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).