Replications for introduction to g-methods: marginal structural models (part 2)

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This tutorial series aims to replicate g-methods explained in this paper by Naimi, A. I., Cole, S. R., and Kennedy, E. H (2017)1 using R. Originally, the paper used SAS to demonstrate g-methods.

In this tutorial, we will focus on replicating the results using marginal structural models to estimate the average causal effects of always taking treatment and compared to never-taking treatment. From our knowledge of the data-generating process, we know this average causal effect to be \(50\). In our tutorial, we will pay more attention to computation rather than proofs to perform replciations.

Reminder: Our Settings
The empirical setting is to treat HIV with a therapy regimen ($A$) in two time periods ($t = 0, t = 1$). Additionally, we measure the time-varying confounder, HIV viral load ($Z$), at times $t = 0$ and $t = 1$. Note that this time-varying confounder is measured before the treatment is administered at each time period. Also, we assume $Z$ at time 0 is 1 (high, bad health condition) for all subjects. Our outcome is the CD4 count (cells/mm$^3$) observed at $t = 2$. Thus, we have: Under the identifying assumptions described in the paper, we will estimate the average causal effect of always taking treatment ($a_0 = 1, a_1 = 1$), compared to never taking treatment ($a_0 = 0, a_1 = 0$) in both time periods. For notation, we are using subscripts to indicate time periods.
  1. Naimi, A. I., Cole, S. R., & Kennedy, E. H. (2017). An introduction to g methods. International journal of epidemiology, 46(2), 756-762.