# Selected Publications

A full list of my publications can be found here.

. Bayesian piecewise linear mixed models with a random change point: an application to BMI rebound in childhood. Epidemiology, 2017.

. Associations between community-level disaster exposure and individual-level changes in disability and risk of death for older Americans. Social Science & Medicine, 2017.

# Recent & Upcoming Talks

A full list of my talks can be found here

simsurv: A Package for Simulating Simple or Complex Survival Data
Thu, Jul 12, 2018
Bayesian joint models for multiple longitudinal biomarkers and a time-to-event outcome: software development and a melanoma case study
Wed, Jul 12, 2017

# Software

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.