Cox’s proportional hazards model is by far the most common approach used to model survival or time to event data. For a simple two group comparison, such as in a randomised controlled trial, the model says that the hazard of failure in one group is a constant ratio (over time) of the hazard of failure in the other group. A test that this hazard ratio equals 1 is a test of the null hypothesis of equality of the survival functions of the two groups. The log rank test is essentially equivalent to the score test that the HR=1 in the Cox model, and is commonly used as the primary analysis hypothesis test in randomised trials.
Covariate adjustment and prediction of mean response in randomised trials
Last week I attended the International Society for Clinical Biostatistics’ conference in Vigo, Spain. I spoke about work I’ve been doing recently on covariate adjusted mean estimation in randomised trials. A pre-print draft of the work is available at arXiv.
Imputing missing covariates in nested case-control and case cohort studies
I’m pleased to announce a new version (1.3.0) of the smcfcs package for multiple imputation of missing covariates. Thanks to Ruth Keogh at the London School of Hygiene & Tropical Medicine, this new version features two additional functions, smcfcs.casecohort and smcfcs.nestedcc. These allow for imputing of missing covariates in case cohort and nested case-control studies respectively. A paper describing the methodology is forthcoming.
The package is now on CRAN and so can be installed/updated in the usual way from R or RStudio.
There are of course various papers on the case cohort and nested case control study designs. For further reading, I’d recommend looking at Ruth’s book, co-authored with David Cox, ‘Case-Control Studies’, which contains a chapter on each design.
11/06/2018 – the corresponding paper has now been published in Biometrics.