PosViolMSM: Positivity Violations in Marginal Structural survival Models

Code for implementing a simulation study to investigate the effect of near 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 (2025). Impact of near-positivity violations on IPTW-estimated marginal structural survival models with time-dependent confounding. Biometrical Journal - in press. [Pre-print version: https://arxiv.org/abs/2403.19606]

Description

• Files:

  • AI_1 simulations.R: Simulation study using Algorithm I - Investigating various scenarios (see Section 4.2.1).
  • AI_2 results.R: Simulation study using Algorithm I - Results (see Section 4.2).
  • AI_3 figures_2_3.R: Results across all scenarios - reproducing Figures 2 and 3 (see Section 4.2.2).
  • AI_4 figures_4_5_6 table_1.R: Focused examination of weight truncation in selected scenarios - reproducing Figures 4 to 6 and Table 1 (see Section 4.2.3).
  • AII_1 simulations.R: Simulation study using Algorithm II - Investigating various scenarios (see Section 5.2.1).
  • AII_results.R: Simulation study using Algorithm II - Results (see Section 5.2).
  • AII_3 figures 7_8.R: Results across all scenarios - reproducing Figures 7 and 8 (see Section 5.2.2).
  • AII_4 figures_9_10_11 tables_2_3.R: Focused examination of weight truncation in selected scenarios - reproducing Figures 9 to 11 and Tables 2 and 3 (see Section 5.2.3).

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

  • algorithm_I.R: Code for Algorithm I (see Section 4.1.2).
  • algorithm_II.R: Code for Algorithm II (see Section 5.1.2).
  • 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.
  • iptw_I.R: Functions to perform IPTW in Simulation Study I.
  • iptw_II.R: Functions to perform IPTW in Simulation Study II.
  • mc_simI_functions.R: Functions to estimate the logit-MSMs using the longitudinal datasets simulated from Algorithm I.
  • mc_simII_functions.R: Functions to estimate the Aalen-MSMs using the longitudinal datasets simulated from Algorithm II.
  • plots_simI.R: Functions to plot results for Algorithm I.
  • plots_simII.R: Functions to plot results for Algorithm II.
  • specific_plots_simI.R: Functions to plot results for Algorithm I for the three specific scenarios examined in Section 4.2.3.
  • specific_plots_simII.R: Functions to plot results for Algorithm II for the three specific scenarios examined in Section 5.2.3.

• After running the main scripts, sub-folder ./results/ will contain the results from the various scenarios and relative performance.

Software

• R software, version 4.3.1
• Packages: data.table, doParallel, foreach, ggplot2, ggpubr, tidyr, timereg.

(Last update: August 18th, 2025)