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This paper examines gender differences according to two new indicators in higher education studies. One indicator represents the length of studies beyond the minimum requirement and the other represents a harmonised graduation mark. We explore these indicators in a statistical framework that consists of Survival analysis Kaplan–Meier methods, Accelerated Failure Time models, Survival Trees, Multivariate Regression Trees and a modified Gender Parity Index. This unique combination of statistical methods allows both to analyse data involving censoring and to incorporate other explanatory variables in the analysis. The approaches were applied to data taken from a Greek and an Italian University and provide evidence that the survival analysis is a useful tool for exploring gender gaps in higher education while the Accelerated Failure Time model permits the investigation of how other variables can influence the gaps. The data mining techniques of survival trees and multivariate regression trees allows for the importance of such influencing variables to be measured and illustrates these in a simple to view tree structure. The Multivariate Regression Tree approach allows us to consider more than one continuous outcome variable so we were able to consider the two proposed indicators simultaneously. Interesting insights from this analysis are that gender has an important role and women outperform regarding these new indicators by taking less length of studies, less graduation time with higher performance, controlling also for other student characteristics. The modified gender parity index enriched the results in all stages. [ABSTRACT FROM AUTHOR]

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time-to-event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real-life data examples that focus on the survival of end-stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed. [ABSTRACT FROM AUTHOR]