Effects of interventions in SAS: GFORMULA and MSM macros

• GFORMULA is a SAS macro to run the parametric g-formula with simulations of population risk under interventions. To download the macros and documentation (version 11 June 2008) click here
• MSM is a SAS macro to run marginal structural models with simulations of population risk under interventions. To download the macros and documentation (version 08 Feb 2008), click here
• Questions? Contact Sarah Taubman
• Note: These macros are intended as a tool to facilitate the use of the g-formula and marginal structural models by other researchers, but in no way constitute a comprehensive piece of software. Please feel free to get in touch with me if you have questions about using the macros in your research.

Marginal structural Cox model in SAS

• This sample program shows how to estimate the parameter of a marginal structural Cox model using inverse probability weighting
• To download, click here
• An earlier version of this program appeared in the appendix of Hernán, Brumback, and Robins (2000)

Structural models for survival analysis in SAS: the SURV macro

• SURV is a set of SAS macros to run either a marginal structural Cox model or a nested structural accelerated failure time model.
• To download, click here (SAS Package IML is needed to run this program)
• Questions? Contact Miguel Hernán or Roger Logan

Nested structural AFT models in STATA: the STGEST command, by Jonathan Sterne and Kate Tilling

• This command implements g-estimation of the parameter of a structural nested accelerated failure time model
• To download the STATA commands and help files, click here
• Lecture Applications of G-estimation using a new Stata command, Harvard School of Public Health, May 21, 2002. Speakers: Jonathan Sterne and Kate Tilling
• Questions? Contact Jonathan Sterne

Marginal structural models in STATA

• The article below shows how to use STATA to fit a marginal structural Cox model
• Fewell Z, Hernán MA, Wolfe F, Tilling K, Choi H, Sterne JAC. Controlling for time-dependent confounding using marginal structural models. STATA Journal 2004; 4: 402-420.