Dr Graeme L. Hickey

Biography

After completing my PhD, I worked as a Post-Doctoral Research Associate at the University of Durham (2010-11), University of Manchester (2011-14), and University of Liverpool (2014-18). In addition, I was Honorary Lecturer at University of Manchester (2014-17) and Honorary Senior Lecturer at University College London (2013-18).

I have extensive experience on both methodological and applied statistics. Methodological research has focused on hierarchical Bayesian modelling, risk prediction modelling, and joint modelling of longitudinal and time-to-event data. Applications have spanned ecotoxicology, cardiovascular surgery, epidemiology, health e-research, public health, and clinical drug trials.

Disclaimer: this website reports my academic contributions only. All views are my own and do not reflect that of my employer.

Interests

Biostatistics
Joint Modelling
R Software
Bayesian statistics
Cardiovascular Surgery Outcomes
Risk Prediction Modelling

Education

CStat, 2016

Royal Statistical Society
PhD in Statistics, 2011

Durham University
MMath in Mathematics, 2006

Durham University

R package updates

Tue, Jul 25, 2017 R R, Joint Modelling

I have submitted updates to both joineR (now version 1.2.1) and joineRML (now version 0.3.0) to CRAN this week. In addition, we now have fantastic looking hex sticker badges from the remarkable hexSticker package. As always, we value user feedback on these packages and will continue to update them.

joineRML v0.3.0

Competing risks in joineR

Wed, Jul 19, 2017 R R, Joint Modelling

Description As of version 1.2.0, joineR fits the joint model proposed by Williamson et al. (2008) for joint models of longitudinal data and competing risks. Here, the longitudinal data submodel remains as per that analyzed in Henderson et al. (2000), namely \[Y_i(t) = X_i(t)^\top \beta + Z_i(t)^\top U_i + \epsilon_i(t),\] where \(Y_i(t)\) is a repeated measurement on subject \(i\) at time \(t\), \(U_i\) is a latent vector that follows a zero-mean multivariate normal distribution, \(X_i(t)\) and \(Z_i(t)\) are vectors of explanatory variables that may be time-constant or time-varying, and the \(\epsilon_i(t)\) are mutually independent errors, \(\epsilon_i(t) \sim N(0, \tau^2)\).

Using joineRML v0.1.1

Fri, Feb 17, 2017 R R, Joint Modelling

The joineRML package implements methods for analyzing data from multiple longitudinal studies in which the responses from each subject consists of time-sequences of repeated measurements and a possibly censored time-to-event outcome. The modelling framework for the repeated measurements is the multivariate linear mixed effects model. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model.

Graeme L. Hickey

Statistics Director

Medtronic, Coronary & Structural Heart

Biography

Interests

Education

Recent Publications

Selected Publications

Statistical primer: checking model assumptions with regression diagnosticss

joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

Finding the forest through the trees in statistics: let the Statistical Primers in EJCTS/ICVTS guide you

Statistical primer: performing repeated-measures analysis

Joint models of longitudinal and time-to-event data with more than one event time outcome: a review

A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial

National registry data and record linkage to inform postmarket surveillance of prosthetic aortic valve models over 15 years

Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery

Surgeon length of service and risk-adjusted outcomes: linked observational analysis of the UK National Adult Cardiac Surgery Audit Registry and General Medical Council Register

How does EuroSCORE II perform in UK cardiac surgery; an analysis of 23,740 patients from the Society for Cardiothoracic Surgery of GB and Ireland National database

Making species salinity sensitivity distributions reflective of naturally occurring communities: using rapid testing and Bayesian statistics

Recent & Upcoming Talks

Recent Posts

R package updates

Competing risks in joineR

Using joineRML v0.1.1

Projects

Cardiovascular Surgery Outcomes

JoineR-M: Joint Modelling of Repeated Measurements and Time-to-Event Data

Medical Statistics Education

Statistical Ecotoxicological Modelling

joineR: Joint Modelling of Repeated Measurements and Time-to-Event Data

joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Teaching

Contact