The goal of this project is to develop novel statistical methodology to a) improve self-management of patients with heart failure, and b) improve prediction of complications in patients with a heart pump. The project is a close collaboration between the Department of Methodology and Statistics (Faculty of Social Sciences, Utrecht University) and the Department of Cardiology (University Medical Center Utrecht).

The PhD project is composed of two related, methodological projects. The first project concerns the improvement of self-management of patients with heart failure. In UMC Utrecht, heart failure (HF) patients can safely monitor and manage their disease using a telemedicine platform called EMPOWER. Using this, patients are enabled to safely act first on changes in their health status: when changes in biometric measurements exceed a clinical threshold, signaling risk, an alert is triggered. Patients can act on these alerts by changing diet, fluid intake, and selected pharmacological therapy themselves. Currently, these thresholds are uniformly prespecified (and thus equal for all patients), and can be manually changed by the patient’s physician or specialist HF nurse. However, thresholds that are set either too wide or too tightly could lead to missing episodes of acute deterioration or too many unhelpful alert triggers. This problem warrants an approach tailored to the individual patient. The goal of this project is to achieve this by extending statistical process control techniques.

The second project concerns the prediction of complications in patients with a heart pump (also known as Left Ventricular Assist Device; LVAD). Major adverse events in patients with LVAD therapy are common and often occur suddenly and unpredictably. However, it is possible to retrieve a wealth of information from the LVAD itself, as well as from patients on LVAD support. Currently, appropriate analysis models to link adverse events to these data are lacking.

Both the EMPOWER platform and the LVAD provide a wealth of intense longitudinal information on biometric measures of the patient. These could be exploited to predict a) automated, personalized and more accurate thresholds for EMPOWER patients, and b) (personalized) clinical outcomes for patients on LVAD. The project comprises three studies that will:

1. develop patient-specific predictions of thresholds/clinical outcomes for separate biometric measurements using proven time series modeling (i.e. statistical process control techniques);
2. develop a statistical model in which separate patient-level biometric measurements are integrated into a single model using machine learning techniques (i.e. Markov related models and/or dynamic factor models). The statistical ensemble model developed for both patient groups could be very similar, or it could be that the data of the EMPOWER and LVAD patients require two different approaches;
3. validate the developed algorithms with feedback loop using a) routinely collected individual outcome data of patients using the EMPOWER platform, or b) alternative sample datasets of patients on LVAD support.

The innovative ensemble model for HF patients based on the EMPOWER data will be integrated in the EMPOWER telemedicine platform for use in clinical settings. The prediction model for complications in LVAD patients will result in early tracking of complications and consequently better perspectives for these patients. Thus, this project will allow you to work on developing novel statistical or machine learning methodology that directly impacts and improves patient care. Findings will be disseminated by scientific reports published in peer-reviewed international journals, and lectures at symposia and annual meetings.

Tasks include:

• conducting the research (literature research, develop and implement a novel analysis method, set up and conduct simulation studies, reporting the results), resulting in international scientific publications and a dissertation;
• teaching (max. 10% per year);
• active participation in the research group of the project and the two departments.

We are looking for an enthusiastic candidate that holds (or nearly holds) a Master’s degree in applied methodology and statistics or a related field such as biostatistics, econometrics, or mathematics. It is considered an advantage if you:

• are proficient in programming with R;
• have an affinity with data science and intensive longitudinal data analysis, for example using Hidden Markov modeling;
• have an interest in medical matters related to heart failure;
• have good social skills;
• are effective and efficient, and able to think conceptually;
• are able to meet deadlines, and conduct research independently and as part of a team consisting of clinicians and methodologists;
• have good communication skills (written and oral) in English.

We offer a temporary position (1.0 FTE) for one year, starting preferably 1 September 2020. Upon a positive performance, the appointment will be extended for three further years. The gross salary - depending on previous qualifications and experience  - ranges between €2,325 and €2,972 (scale P according to the Collective Labour Agreement Dutch Universities) per month for a full-time employment. Salaries are supplemented with a holiday bonus of 8% and a year-end bonus of 8.3% per year. In addition, Utrecht University offers excellent secondary conditions, including an attractive retirement scheme, (partly paid) parental leave and flexible employment conditions (multiple choice model). More information about working at Utrecht University can be found here.

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University, the various disciplines collaborate intensively towards major societal themes. Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Sustainability.

The Faculty of Social and Behavioural Sciences is one of the leading faculties in Europe providing research and academic teaching in Cultural Anthropology, Educational Sciences, Interdisciplinary Social Science, Pedagogical Sciences, Psychology, and Sociology. More than 5,600 students are enrolled in a broad range of undergraduate and graduate programmes. The Faculty of Social and Behavioural Sciences has about 850 faculty and staff members, all providing their individual contribution to the training and education of young talent and to the research into and finding solutions for scientific and societal issues. 

The PhD student will be employed at the Department of Methodology and Statistics of the Faculty of Social and Behavioural Sciences at Utrecht University. The research of the Department of Methodology and Statistics focuses on a broad array of methods and techniques for the social and behavioural sciences and comprises topics like: longitudinal studies, Mplus and multilevel analyses, collection and analysis of intensive big data, survey research, research synthesis techniques, best practices when doing research, and qualitative research and mixed methods research. In addition, the Department of Methodology & Statistics provides teaching in methods and statistics within all Bachelor’s and Master’s degree programmes of the Faculty of Social and Behavioural Sciences and University College Utrecht. The department also advises staff and students with respect to their research activities. The PhD candidate will find a dynamic and pleasant working environment, in a group that is actively involved in scientific research at the highest international level. In addition, the candidate will become a member of the Interuniversity Graduate School of Psychometrics and Sociometrics. The statistical supervisory team will include Dr Emmeke Aarts (daily supervisor) and Dr Daniel Oberski.

The PhD student will be holding a guest appointment at the Department of Cardiology, University Medical Center Utrecht. University Medical Center Utrecht (UMCU) is one of the eight academic centers in the Netherlands with almost 12,000 employees. Patient care and biomedical research are closely linked, resulting in a fertile environment carefully advancing science from bench to bedside. The Faculty of Medicine of Utrecht University is an integrated part of the UMCU. The UMCU supports 6 research areas including “Center for Circulatory Health”. The Circulatory Health programme focuses on prediction, prognosis and prevention of cardiovascular disease with a special focus on the diagnosis, monitoring and treatment of heart failure. The clinical supervisory team of UMCU will include cardiologists Dr Linda van Laake and Prof Folkert Asselbergs.

Utrecht is a young and vibrant city with a large academic population, around 30 minutes south of Amsterdam. It combines a beautiful old city centre with a modern university. Utrecht has an excellent quality of life, with plenty of green space and a strong bicycle culture.