Jonathan Bartlett

PhD on causal inference for competing risks data

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Applications are invited for a 3-year PhD studentship from the ESRC UBEL DTP (UCL, Bloomsbury and East London Doctoral Training Partnership)

We are seeking applicants who would like to pursue PhD research on the project described below. This project is offered as part of the Longitudinal Analysis and Design topic under the Quantitative Social Science Pathway of the ESRC UBEL DTP. Successful applicants will based in the Department of Medical Statistics at LSHTM. Further information on the funding scheme can be found at https://ubel-dtp.ac.uk/esrc-studentships/

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Mixed models repeated measures (mmrm) package for R

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I was recently made aware of the release of the mmrm package in R. It has been developed by a group of programmers and statisticians at a number of pharmaceutical companies, led by Daniel Sabanes Bove at Roche, as part of the ASA Biopharmaceutical Section Software Engineering Working Group. I’ve written previously about fitting mixed models for repeated measures (MMRM) using R, Stata and SAS. In R, this can be done using the gls function in the nlme package, but there are a number of limitations with this approach. For example, it is difficult (or impossible) to fit models where you allow the covariance parameters to be distinct between treatment groups. In this post, I’ll take a very quick look at the new mmrm package in R.

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Causal (in)validity of the trimmed means estimand

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This week I’ve been given the opportunity to present some ongoing work with colleagues Camila Olarte Parra and Rhian Daniel about the so called ‘trimmed means estimand’ in clinical trials at the International Biometric Conference in Riga, Latvia. The slides of my talk are available here for anyone interested. In this post I’ll give a brief overview of my talk.

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