I’ve recently had the opportunity to spend a little time looking at an interesting approach for improving the efficiency of estimated treatment effects in clinical trials which exploits historical data. In this blog post I’ll give a few thoughts on the results in ‘Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score’, by Schuler et al 2022. The paper was published in the International Journal of Biostatistics, and an arXiv pre-print is available here.
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.
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.