PosViolMSM: Positivity Violations in Marginal Structural survival Models

Code for implementing a simulation study to investigate the effect of positivity violations on the estimates from IPTW-based marginal structural survival models in a survival context with longitudinal exposure and time-dependent confounding.

Reference

Spreafico M (2024). Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance. https://arxiv.org/abs/2403.19606

Description

• Files:

  • AI_simulations.R: Simulation study using Algorithm I - Investigating various scenarios (see Section 4.1.1).
  • AI_results.R: Simulation study using Algorithm I - Results (see Sections 4.1.2 and 4.1.3).
  • AII_simulations.R: Simulation study using Algorithm II - Investigating various scenarios (see Section 4.2.1).
  • AII_results.R: Simulation study using Algorithm II - Results (see Sections 4.2.2 and 4.2.3).

• Sub-folder ./functions/ contains some auxiliary functions to run the main files:

  • algorithm_I.R: Code for Algorithm I (see Section 3.2.2).
  • algorithm_II.R: Code for Algorithm II (see Section 3.3.2).
  • eval_measuresI.R: Functions to evaluate the results for the scenarios simulated using Algorithm I.
  • eval_measuresII.R: Functions to evaluate the results for the scenarios simulated using Algorithm II.
  • ms_simI_functions.R: Functions to estimate the logit-MSMs using the longitudinal datasets simulated from Algorithm I.
  • ms_simII_functions.R: Functions to estimate the Aalen-MSMs using the longitudinal datasets simulated from Algorithm II.

• Sub-folder ./results/ contains the results from the various scenarios and relative performance.

Software

• R software.
• Packages: data.table, survival, tidyr, timereg.

(Last update: March 29th, 2024)