Randomisation as the basis for inference in trials

Today I was lucky enough to listen Prof William Rosenberger present the 15th Armitage lecture in Cambridge. Prof Rosenberger has worked extensively on randomisation in trials in various respects (see his book), and he delivered a really interesting talk. The talk can now be viewed online here.

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Live stream seminar 26th October 2017: Covariate adjustment and prediction of mean response in randomised trials

Next Thursday (14:00 UK time, 26th October 2017) I'll be giving a seminar at the University of Southampton in the UK on my recent work on covariate adjustment and prediction of mean response in randomised trials. The seminar will be live streamed here, which after the talk will be accessible as a recording.

The talk abstract can be found here.

Testing equality of two survival distributions: log-rank/Cox versus RMST

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.

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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.

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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.

Odds ratios, collapsibility, marginal vs. conditional, GEE vs GLMMs

Generalised estimating equations (GEEs) and generalised linear mixed models (GLMMs) are two approaches to modelling clustered or longitudinal categorical outcomes. Here I will focus on the common setting of a binary outcome. As is commonly described, the two approaches estimate different effect measures, with GEEs targeting so called marginal effects, and GLMMs targeting conditional or subject specific effects. Understanding the difference between these is potentially quite tricky I think.

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