I am a postdoctoral Research Fellow in biostatistics at the School of Public Health and Preventive Medicine at Monash University in Melbourne, Australia. I am also a member of the Victorian Centre for Biostatistics (ViCBiostat).
I have a keen interest in survival analysis, joint longitudinal-survival models, models for longitudinal data from cohort studies, Bayesian inference, and more recently, in the design of Bayesian adaptive clinical trials. My PhD was entitled “Joint longitudinal and time-to-event models: development, implementation and applications in health research” and was supervised by Prof Rory Wolfe (primary), Dr Margarita Moreno-Betancur, and Dr Michael Crowther. I am a regular user of Stata, R and Stan, and have contributed software packages to the latter two. I have previously worked as both an academic and consultant biostatistician in a number of different roles; my full CV can be found here.
PhD in Biostatistics, 2018
Monash University, Australia
MSc in Medical Statistics, 2015
University of Leicester, UK
BSc(Hons) in Statistics, 2007
University of Otago, NZ
A full list of my publications can be found here.
A full list of my talks can be found here
simsurv is an R package for simulating survival (i.e. time-to-event) data. The user can simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard or log hazard function. The latter two features are those which likely separate the
simsurv package from other packages available for simulating survival data in R. The package implements the methods described in Crowther and Lambert (2013) and is modelled on the
survsim package available in the Stata software.
rstanarm is an extensive R package for Bayesian applied regression modelling. It is written and maintained by Ben Goodrich and Jonah Gabry. However, I have contributed code for fitting multivariate mixed models (the
stan_mvmer modelling function) and joint longitudinal and time-to-event models (the
stan_jm modelling function), as well as a number of post-estimation functions for obtaining predictions and diagnostics for the fitted models.
simjm is an R package package that allows the user to simulate data from a shared parameter joint model for longitudinal and time-to-event data. The shared parameter joint model from which the simulated data is generated is based on the model formulation described for the
stan_jm modelling function in the
rstanarm R package. The shared parameter joint model can be univariate (i.e. one longitudinal marker) or multivariate (i.e. more than one longitudinal marker) and a variety of parameterisations are allowed for the association structure between the longitudinal and event submodels.
devr2 is a Stata module that can be used to calculate a deviance based R-squared measure for models estimated using Stata’s
glm command. The measure is based on the method of Cameron and Windmeijer (1997). The module can be easily installed from within your Stata session; simply type
ssc install devr2 into the Command window.