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.

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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Confidence intervals for the hazard ratio in RCTs which agree with log rank test

The log rank test is often used to test the hypothesis of equality for the survival functions of two treatment groups in a randomised controlled trial. Alongside this, trials often estimate the hazard ratio (HR) comparing the hazards of failure in the two groups. Typically the HR is estimated by fitting Cox's proportional hazards model, and a 95% confidence interval is used to indicate the precision of the estimated HR.

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Estimands and assumptions in clinical trials

Today I listened to a great Royal Statistical Society webinar, with Alan Phillips and Peter Diggle (current RSS president) presenting. The topic was a particularly hot one in the clinical trials world right now, namely estimands.

Alan's presentation gave an excellent overview of the work of a PSI/EFSPI special interest group on estimands. Topics discussed included defining exactly what is meant by an estimand, whether there should be a standardised set of estimands which could be used across trials conducted in different disciplines, and what the estimand discussion means in terms of implementation and statistical analysis.

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Matching analysis to design: stratified randomization in trials

Yesterday I was re-reading the recent nice articles by Brennan Kahan and Tim Morris on how to analyse trials which use stratified randomization. Stratified randomization is commonly used in trials, and involves randomizing in a certain way to ensure that the treatments are assigned in a balanced way within strata defined by chosen baseline covariates.

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