I’ve previously written about methods from causal inference (G-formula and G-estimation) that can be used to exploit data observed after patients experience intercurrent events (ICEs) to improve the precision (and hence statistical power) of estimates of treatment effects in clinical trials. Thanks to useful comments from reviewers, in our revised paper (open access version) on the topic, we explore the robustness of these methods, and those that do not make use of such data, to misspecification in model assumptions.
How to interpret hazard ratios
Survival analysis of time-to-event outcomes is very commonly performed using Cox’s famous proportional hazards model. The model estimates hazard ratios for the ‘effects’ of covariates. Starting with Hernán’s ‘Hazard of Hazard Ratios’ paper, hazard ratios have been investigated and critiqued from a causal inference perspective. Following this, Aalen wrote an important paper on whether’s analysis of a randomised trial using Cox’s model yields a causal effect, and there have been a number of more recent papers investigating the issue further. The criticisms and complexity arise due to the definition of the hazard and the presence of so-called frailty factors – unmeasured variables which influence when someone has the event of interest.
I had briefly blogged about this topic before, in particular about the causal interpretation of the hazard ratio when the proportional hazards assumption holds. I’m really pleased to have now (finally!) finished a short expositional article with colleagues Dominic Magirr and Tim Morris about how we think hazard ratios should be interpreted. Using a simple example we review the key issue arising from the effects of frailty, articulate how we think hazard ratios ought to be interpreted, and argue that it should be viewed as a causal quantity. A pre-print of our article is available now on arXiv.
Research Fellow post at LSHTM – machine learning for missing data
We are currently recruiting for a Research Fellow position at London School of Hygiene & Tropical Medicine to work on an exciting new project that will develop machine learning based methods for handling missing data in statistical analyses. The project, funded by the UK’s Economic and Social Research Council, will develop new missing data methods based on recently developments in double or debiased machine learning. The project team includes myself (Jonathan Bartlett), Shaun Seaman at the MRC Biostatistics Unit, and Richard Silverwood from UCL.
The post will be for 3.5 years, and we are accepting applications until 30th September. For further details on the role and to apply, please see the LSHTM jobs site.