Sullivan and colleagues have recently published a nice paper exploring multiple imputation for missing covariates or outcome when one is interested in estimating relative risks. They performed simulations where missing covariates or outcomes were imputed either using multivariate normal imputation or using fully conditional specification imputation (FCS), and where the true outcome model is a log link binomial model. They concluded that multivariate normal imputation performed poorly, producing estimated coefficients which were biased towards the null. Fully conditional specification performed better, although estimates were still biased in certain situations.
Causal interpretation of the hazard ratio from RCTs when proportional hazards holds
In 2015 I wrote a post about the causal interpretation of hazard ratios estimated in randomised trials, following a paper by Aalen and colleagues. One of the arguments made in that paper was that the hazard ratio does not have a valid interpretation as a causal effect in this setting, even when the proportional hazards assumption holds:
This makes it unclear what the hazard ratio computed for a randomized survival study really means. Note, that this has nothing to do with the fit of the Cox model. The model may fit perfectly in the marginal case with X as the only covariate, but the present problem remains.
With recent discussions on estimands in light of the estimand addendum to ICH E9, I have been thinking more on the argument/claim by Aalen et al.