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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Multiarm trials - should we allow for multiplicity?

Last week I listened to a great presentation about new trial designs by Mahesh Parmar, director of the Medical Research Council Clinical Trial Unit in London. Among the topics he touched on were multi-arm trials (and extensions), as an attractive alternative to the classic two arm trial. There seem to be a number of advantages to such a trial design, in which in the simplest case, the trial randomizes patients to either control or one of a number of experimental treatments.

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Improving efficiency in RCTs using propensity scores

Propensity scores have become a popular approach for confounder adjustment in observational studies. The basic idea is to model how the probability of receiving a treatment or exposure depends on the confounders, i.e. the 'propensity' to be treated. To estimate the effect of exposure, outcomes are then compared between exposed and unexposed who share the same value of the propensity score. Alternatively the outcome can be regressed on exposure, weighting the observations using the propensity score. For further reading on using propensity scores in observational studies, see for example this nice paper by Peter Austin.

But the topic of this post is on the use of propensity scores in randomized controlled trials. The post was prompted by an excellent seminar recently given by my colleague Elizabeth Williamson, covering the content of her recent paper 'Variance reduction in randomised trials by inverse probability weighting using the propensity score" (open access paper here).

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