G-formula (sometimes known as G-computation) is an approach for estimating the causal effects of treatments or exposures which can vary over time and which are subject to time-varying confounding. It is one of the so called G-methods developed by Jamie Robins and co-workers. For a nice overview of these, I recommend this open access paper by Naimi et al 2017, and for more details, the What If book by Hernán and Robins. In this post, I’ll describe some recent work with Camila Olarte Parra and Rhian Daniel in which we have explored the use of multiple imputation methods and software as a route to implementing G-formula estimators.
Multiple imputation for missing baseline covariates in discrete time survival analysis
A while ago I got involved in a project led by Anna-Carolina Haensch and Bernd Weiß investigating multiple imputation methods for baseline covariates in discrete time survival analysis. The work has recently been published open access in the journal Sociological Methods & Research. The paper investigates a variety of different multiple imputation approaches. My main contribution was the extension of the substantive model compatible fully conditional specification (smcfcs) approach for multiple imputation to the discrete time survival model setting, and extending the functionality of the smcfcs package in R to incorporate this. In this short post I’ll give a quick demonstration of this functionality.
‘An introduction to covariate adjustment in trials’ – PSI covariate adjustment event
Later today I’m delighted to be participating in an online event organised by the Statisticians in the Pharmaceutical Industry (PSI) on the topic of covariate adjustment in clinical trials. The slides of my introduction talk can be viewed and downloaded below. In the talk I’ll very briefly be covering
- conditional and marginal effects/estimands
- reasons to adjust analyses of randomised trials for baseline covariates
- robustness properties of linear regression models
- interpretation of effect estimates from regression models
- the standardisation / G-formula estimator of marginal effects, as mentioned in the FDA’s covariate adjustment guidance
- some properties of treatment effect estimators when stratified randomisation is used
One aspect that I unfortunately will only have a little time to touch on is the ongoing debate about marginal vs. conditional estimands. For a bit more discussion on this point, see this previous blog post.