For more information please contact Prof. dr. Iven Van Mechelen, tel.: +32 16 32 61 31, mail: firstname.lastname@example.org or Prof. dr. Geert Verbeke, tel.: +32 16 37 33 33, mail: email@example.com.
You can apply for this job no later than March 31, 2019 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
The successful candidate will work in the Research Group of Quantitative Psychology and Individual Differences (https://ppw.kuleuven.be/okp/home/) in close collaboration with the Leuven Biostatistics and statistical Bioinformatics Centre (http://ibiostat.be/l-biostat). The candidate will do so under the supervision of Iven Van Mechelen, Geert Verbeke, and Geert Molenberghs.
The successful candidate will work in the Research Group of Quantitative Psychology and Individual Differences (https://ppw.kuleuven.be/okp/home/) in close collaboration with the Leuven Biostatistics and statistical Bioinformatics Centre (http://ibiostat.be/l-biostat). The candidate will do so under the supervision of Iven Van Mechelen, Geert Verbeke, and Geert Molenberghs. The groups offer an international, productive, collaborative, and interactive environment with 10 faculty, 26 graduate and postgraduate students, and 4 statistical consultants. The University of Leuven is a research-oriented institution and is consistently ranked among the top research universities in Europe. Leuven is one of the oldest university towns in Europe, about 30 km from Brussels. It has a rich history and a unique friendly atmosphere.
For many health problems, multiple treatment alternatives are available. A major challenge then is to identify optimal decision rules or treatment regimes that specify the preferable treatment alternative for each individual patient, based on that patient’s pattern of pretreatment characteristics. An optimal decision rule is one leading to the best possible result when applied to the whole target population of patients under study. While everybody agrees that personalized treatment decisions are very important, there currently is a considerable risk that decisions based on estimated treatment regimes are suboptimal, in that many patients would not receive the best possible treatment. This may imply a high human and financial cost, and may therefore be detrimental for both the individual patient and society. The present project will study this problem thoroughly and will propose solutions to it. In particular, it will contribute: (1) a better understanding of whether and why the rules that result from state-of-the-art methods to look for optimal decision rules are far from truly optimal; (2) new, effective tools (along with accessible and user-friendly software packages) for tracing truly optimal rules, for drawing correct conclusions about whether the truly best rule is a one-size-fits-all or a personalized one, and for assessing the quality of estimated decision rules. For this purpose it will rely on in-depth mathematical-statistical analyses of the theoretical basis of the problems at hand (starting from derivations within elementary settings) as well as on extensive simulation-based benchmarking studies. In addition, the newly developed tools will be applied to already available data from randomized clinical trials on ADHD and depression.
Prerequisites for candidates on this project are a PhD with a strong basis in mathematical statistics, a solid background in algorithmics and programming, and excellent English reporting skills as evidenced by a very good publication track record in peer-reviewed first rank international journals.
We offer a 3-year postdoc position (starting on October 1, 2019 or as soon as possible), an encouraging guidance, facilities for presenting research results at international conferences, and a competitive salary with various additional benefits (in terms of holidays, health insurance, and transport costs).