I’m an Associate Professor of Biostatistics in the Biostatistics Research Group at the University of Leicester. Since August 2014, I’ve been a Section Editor of the Journal of Statistical Software, and since January 2018, I am an Associate Editor of the Stata Journal. My main research interests include survival analysis, multilevel and mixed effects models, and statistical software development. I lead a programme of research developing methodology for the analysis of complex survival data, motivated by applications to electronic health records.
After completing my PhD on complex survival and joint longitudinal-survival models, which can be downloaded here, I did a post-doc at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet in Stockholm, before returning to Leicester in March 2016 to take up a lectureship.
PhD in Medical Statistics, 2014
University of Leicester
MSc in Medical Statistics, 2010
University of Leicester
MMath in Mathematics and Statistics, 2009
University of St. Andrews
merlincan do a lot of things. From simple stuff, like fitting a linear regression or a Weibull survival model, to a three-level logistic mixed effects model, or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with non-linear effects…the list is rather endless.
merlincan do things I haven’t even thought of yet. I’ll take a single dataset, and attempt to show you the full range of capabilities of
merlin, and discuss some future directions for the implementation in
mestregcommand to fit multilevel mixed effects parametric survival models, assuming normally distributed random effects, estimated with maximum likelihood utilising Gaussian quadrature. In this article, I present the user written
stmixedcommand, which serves as both an alternative and a complimentary program for the fitting of multilevel parametric survival models, to
mestreg. The key extensions include incorporation of the flexible parametric Royston-Parmar survival model, and the ability to fit multilevel relative survival models. The methods are illustrated with a commonly used dataset of patients with kidney disease suffering recurrent infections, and a simulated example, illustrating a simple approach to simulating clustered survival data using
survsim(Crowther and Lambert, 2012, 2013).
As part of my research I have developed a range of software packages in Stata. More details, including tutorials, can be found on the package-specific pages:
megenreg ~ mixed effects regression for linear and non-linear models - this has been superseded by
staft ~ flexible parametric accelerated failure time models
stjm ~ joint models of longitudinal and survival data
stgenreg ~ general parametric survival models
stmixed ~ multilevel parametric survival models
stmix ~ two-component mixture parametric survival models
extfunnel ~ extended funnel plots for meta-analysis
metapow ~ simulation-based sample size calculations for designing trials based on an existing meta-analysis
Each package can be installed by typing
ssc install cmdname within Stata. Having said that, I’m starting to move things over to git repositories, so keep an eye on the package pages for installation instructions.
Manipulating model objects
A major update to the multistate package in Stata, and other news in my multistate world
My core teaching is on the MSc Medical Statistics course at the University of Leicester.
I teach a number of short courses, some teaching material is made freely available on the course pages:
A list of all my talks can be found here.
Dr Emma Martin, Post-doctoral Research Associate in Biostatistics, University of Leicester. Emma is funded by my MRC New Investigator Research Grant to work on a variety of projects in multi-state survival models and joint models.
Micki Hill, Research Assistant in Biostatistics, University of Leicester. I co-supervise Micki who is funded by the charity Duchenne UK, to work on Project HERCULES - a multi-disciplinary collaborative project, where she’ll be working on multi-state survival models.
Jonathan Broomfield, NIHR Methods Fellow, University of Leicester.
As main supervisor:
Alessandro Gasparini, University of Leicester (1st October 2016 - Present)
Alessandro has been working on frailty survival models and a RShiny app for use in summarising simulation studies. His main project centres on informative observations in joint modelling of longitudinal and survival data. More details on his PhD can be found here.
Nuzhat Ashra, University of Leicester (25th September 2017 - Present).
Nuzhat is funded by an MRC IMPACT studentship and SPD Development Company, to work on joint modelling of biomarkers to predict miscarriage. She will also be working on some extensions to the
stjm command in Stata, including dynamic predictions.