On the ranking of Test match batsmen

Ranking sportsmen whose careers took place in different eras is often a contentious issue and the topic of much debate. In this paper we focus on cricket and examine what conclusions may be drawn about the ranking of Test batsmen using data on batting scores from the first Test in 1877 onwards. The overlapping nature of playing careers is exploited to form a bridge from past to present so that all players can be compared simultaneously, rather than just relative to their contemporaries. The natural variation in runs scored by a batsman is modelled by an additive log-linear model with year, age and cricket-specific components used to extract the innate ability of an individual cricketer. Incomplete innings are handled via censoring and a zero-inflated component is incorporated into the model to allow for an excess of frailty at the start of an innings. The innings-by-innings variation of runs scored by each batsman leads to uncertainty in their ranking position. A Bayesian approach is used to fit the model and realisations from the posterior distribution are obtained by deploying a Markov Chain Monte Carlo algorithm. Posterior summaries of innate player ability are then used to assess uncertainty in ranking position and this is contrasted with rankings determined via the posterior mean runs scored. Posterior predictive checks show that the model provides a reasonably accurate description of runs scored

Planning for optimal performance – what happens before the taper?

This study analysed contemporary performance data of middle distance athletes to determine a) the number of competitive performances prior to the season's fastest performance and b) the time frame between their first and fastest competitive performances of that season. Using a publicly available database, data on the number of races and days between athletes first and fastest races were extracted. The analysis utilised 4,800 observations from 1,166 individual athletes for the period 2006 to 2017. Male 800 m athletes achieved their fastest performance in 8 races (IQR=4-12) distributed over 55 days (IQR=29-87). Female 800 m athletes also required 8 races (IQR=5-12), distributed over 63 days (IQR=34-91). In the 1500 m event, male athletes, required 6 races (IQR=3-9) over 48 days (IQR=25-76), while female athletes, required 7 races (IQR=4-10) over 56 days (IQR=28-84). For both sexes, 1500 m athletes raced less, and over a shorter period than 800 m athletes before reaching their fastest performance. Female athletes, had a longer time frame and number of races than male athletes. This study provides an evidence-based indicator of when a middle distance runner’s fastest performance is likely to occur, providing benchmarks that could act as a guide for coaches when designing competition programmes, prior to any tapering process

Impact arising from sustained public engagement: A measured increase in learning outcomes

This article details the impact arising from a sustained public engagement activity with sixth-form students (16 to 17 year olds) across two Further Education Colleges during 2012/13. Measuring the impact of public engagement is notoriously difficult. As such, the engagement programme closely followed the recommendations of the National Co-ordinating Centre for Public Engagement (NCCPE) and their guidance for assessing Research Excellence Framework 2014 (REF2014) impact arising from public engagement with research. The programme resulted in multiple impacts as defined by the REF2014 under “Impacts on society, culture and creativity”. Specifically: the beneficiaries’ interest in science was stimulated; the beneficiaries’ engagement in science was improved; their science-related education was enhanced; the outreach programme made the participants excited about the science topics covered; the beneficiaries’ awareness and understanding was improved by engaging them with the research; tentative evidence of an improvement in AS-level grades; indirect evidence of an improvement in student retention. These impacts were evidenced by the user feedback (i.e. sixth-form students) collected from 50 questionnaires (split 16 and 34 across the two Further Education Colleges), as well as testimonies from both the teachers and individual participants

joineR: Joint modelling of repeated measurements and time-to-event data

The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. 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

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

Fits the joint model proposed by Henderson and colleagues (2000) (doi:10.1093/biostatistics/1.4.465), but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project is funded by the Medical Research Council (Grant number MR/M013227/1)

Review Article

<p>Coefficients of variables in the beta-regression model of ADS.</p

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

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

Background - Available methods for the joint modelling of longitudinal and time-to-event outcomes have typically only allowed for a single longitudinal outcome and a solitary event time. In practice, clinical studies are likely to record multiple longitudinal outcomes. Incorporating all sources of data will improve the predictive capability of any model and lead to more informative inferences for the purpose of medical decision-making. Methods - We reviewed current methodologies of joint modelling for time-to-event data and multivariate longitudinal data including the distributional and modelling assumptions, the association structures, estimation approaches, software tools for implementation and clinical applications of the methodologies. Results - We found that a large number of different models have recently been proposed. Most considered jointly modelling linear mixed models with proportional hazard models, with correlation between multiple longitudinal outcomes accounted for through multivariate normally distributed random effects. So-called current value and random effects parameterisations are commonly used to link the models. Despite developments, software is still lacking, which has translated into limited uptake by medical researchers. Conclusion - Although, in an era of personalized medicine, the value of multivariate joint modelling has been established, researchers are currently limited in their ability to fit these models routinely. We make a series of recommendations for future research needs

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

Background: Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML. Results: A multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers. Conclusions: An open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed

Higher education outreach: examining key challenges for academics

How should academic staff engage in outreach with communities outside of the university? The need of academics to answer this question has intensified in the UK given the changing priorities of academic job roles, shaped by increasing institutional concern for widening participation, graduate employability and research impact in an era of austerity and high tuition fees. While university outreach professionals, such as those in widening participation, have access to a range of networks, resources and support mechanisms for outreach activity, academics often face a series of profession-specific pressures that make engagement in outreach complex and contingent. This article draws upon the experience of 25 academics from 18 different subject areas and 18 institutions to examine and provide responses to key challenges faced by academics involved in outreach in the UK. We examine such issues as: the conceptualisation of outreach; funding; recognition and management of workload; nurturing relationships with internal and external partners; capacity-building; commercial interests, payment and responsibility; pedagogical style and content; integration of outreach into curricula, and evaluation of programmes. The examination offered is not all encompassing, but acts as a series of reference points to consider the challenges faced by UK academics in an evolving outreach sector