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Lecturer in Statistics: Dr Ardo van den Hout

Name

Dr Ardo van den Hout

Career profile 

Ardo started studying at the University of Nijmegen in 1989. “In the Netherlands, there was quite a bit of freedom at that time to plan your study. I took my time, and studied Philosophy (MA, January 1996) and Mathematics (MSc, March 1997)”.

“I didn’t really know what to do after graduating in mathematics. I ended up doing a two-year post-master programme to make the step from pure mathematics to applied mathematics. Part of this programme was a one-year internship at a research institute, which I took up at Statistics Netherlands (the Dutch office for national statistics). From the internship followed a PhD project in collaboration with Utrecht University”.

In 2005, Ardo moved to the UK.  “I was offered a Career Development Fellowship for three years in the MRC Biostatistics Unit in Cambridge. It was only after working there for a couple of months that I realised what I great opportunity this was. It was a big unit with research as its main focus. There was a wealth of statistical knowledge across the different research programmes, and a very good, highly-customised system for statistical computing”.

“My PhD in the Netherlands was on methods for social statistics. One reason to start in Cambridge was my aim to learn more about biostatistics. The Career Development Fellowship was very suitable for this as it allowed me time to learn new methods and to extend my education by attending workshops and scientific conferences”.

“The three years as postdoc were followed by a further three years as a researcher. This was six years of research only. Needless to say I learned a lot. I was also able to get research published in statistical journals. All of this was joint work, mostly with people in the Biostatistics Unit but also with other collaborators”. 

“My research in Cambridge was on methods for longitudinal data analysis, with applications in ageing research. An example of this is specification and estimation of multi-state survival models.  A model with three states, for instance, can be defined by a healthy state, an ill-health state, and the dead state. Such a model can be used to describe, understand, and predict change of health over time. An important application here is predicting years spent in the healthy state given a specified age. These future years spent in the healthy state can be interpreted as residual healthy life expectancy. Healthy life expectancies are increasingly used to investigate average levels of health in the older population.  This is applied to regions in the UK, but also on a more global scale to compare countries. The idea here is that it is not only important to know how long people live, but also to know which part of the remaining years to live will be spent in good health”.

“I always wanted to get into academic teaching, so a lectureship was the obvious next step. At UCL, my research is a continuation of the research that started in the MRC Biostatistics Unit. For my teaching, it is of great value that I have worked with data in an applied setting. In the Department of Statistical Science at UCL we expect students to be good at mathematical aspects of statistics, but it is equally important that students realise how it works when statistics is applied. My past work for the MRC helps me to teach applied statistics, to explain the many possibilities but also to warn for limitations”.

Correct as of: April 2015