Joint modelling of multivariate longitudinal and time-to-event data

Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, University of Durha

Joint modelling of multivariate longitudinal and time-to-event data

Invited presentation by the Statistics Research Group at the Department of Mathematical Sciences, University of Durha

UK Heart Surgery: What Patients Can Expect from their Surgeons

This report presents selected findings from the National Adult Cardiac Surgery Audit for heart operations that took place between 2001/2 and 2010/11, alongside other information about cardiac surgery in the UK

On the quantification of intertest variability in ecotoxicity data with application to species sensitivity distributions

Ecotoxicological hazard assessment relies on species effect data to estimate quantities such as the predicted no-effect concentration. While there is a concerted effort to quantify uncertainty in risk assessments, the uncertainty due to intertest variability in species effect measurements is an overlooked component. The European Union Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) guidance document suggests that multiple toxicity records for a given chemical–species combination should be aggregated by the geometric mean. Ignoring this issue or applying unjustified so-called harmonization methods weakens the defensibility of uncertainty quantification and interpretation about properties of ecological models, for example, the predicted no-effect concentration. In the present study, the authors propose a simple and broadly theoretically justifiable model to quantify intertest variability and analyze it using Bayesian methods. The value of data in ecotoxicity databases is maximized by using (interval-)censored data. An exploratory analysis is provided to support the model. The authors conclude, based on a large ecotoxicity database of acute effects to aquatic species, that the standard deviation of intertest variability is approximately a factor (or fold-difference) of 3. The consequences for decision makers of (not) adjusting for intertest variability are demonstrated. Environ. Toxicol. Chem. 2012; 31: 1903–1910. © 2012 SETA

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

Methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy