A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects

<p>Abstract</p> <p>Background</p> <p>Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty.</p> <p>Methods</p> <p>MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework.</p> <p>Results</p> <p>Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN.</p> <p>Conclusion</p> <p>The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.</p

Equity in mathematics and science outcomes: characteristics associated with high and low achievement on PISA 2006 in Ireland

Equity in education is a key concern internationally; however, it is rare that this issue is examined separately for low- and high-achieving students and concurrently across different subject domains. This study examines student and school background characteristics associated with low and high achievement in mathematics and science on the Programme for International Student Assessment. Based on the results of a multilevel multinomial model of achievement for each domain, findings indicate that a greater number of the variables examined are associated with low rather than high achievement. At student level, home language, intention to leave school early, socioeconomic status, grade level, cultural capital, and books in the home are significantly associated with achievement in mathematics and science. At school level, only school average socioeconomic status is statistically significant in the models. Significant gender differences are found in the distribution of high and low achievers, which vary across the domains. In mathematics, females are more likely to be low achievers while males are more likely to be high achievers. In science, gender interacts with early school-leaving intent whereas males intending to leave school early are more likely to be in the low-achieving group than females intending to leave early. Conclusions emphasise the need for targeting resources aimed at promoting equity in outcomes at student level as well as at school level. Future work may extend the current analyses by incorporating domain-specific variables or examining cross-country differences

Value-added measures for schools in England: looking inside the ‘black box’ of complex metrics

Value-added measures can be used to allocate funding to schools, to identify those institutions in need of special attention and to underpin government guidance on targets. In England, there has been a tendency to include in these measures an ever-greater number of contextualising variables and to develop ever-more complex models that encourage (or ‘impose’) in schools a single uniform method of analysing data, but whose intricacies are not fully understood by practitioners. The competing claims of robustness, usability and accessibility remain unresolved because it is unclear whether the purpose of the measurement is teacher accountability, pupil predictability or school improvement. This paper discusses the provenance and shortcomings of value-added measurement in England (and the Pupil Level Annual Schools Census that informs it) including the fact that although the metrics are essential for School Effectiveness Research, they fail to capture in its entirety the differential effectiveness of schools across the prior attainment range and across sub-groups of students and subjects