Decision support algorithms can be very useful, especially in important areas like healthcare. But they also pose the risk of propagating historic biases. This project aims to make such algorithms more transparent and reliable. We will develop a new statistic that explains to a particular person seeking advice from the algorithm, how many similar persons contributed data to the algorithm. This allows citizens to judge for themselves whether an algorithm sufficiently applies to their individual situation. Knowing this, citizens can choose if they want to use the algorithm in their decision making or not.

Flexible and user-adaptive statistical inference In this proposal we want to develop mathematical theory for testing many hypotheses simultaneously. Our new theory brings a threefold flexibility into the research: in terms of sample size, analysis and model choice. The researcher may look at the data and the intermediate results, and decide on that basis to add more data, to add more hypotheses – or focus on the most promising ones – and she does not have to restrict herself to a specific pre-specified model. In the mean time our methods retain strong statistical error guarantees.

Breaking barriers in survival analysis: Unlocking the potential of additive hazards models through maximum likelihood The dominant model for time-to-event data is Cox’s proportional hazards model. It is immensely popular because it yields a single effect-measure for the effect of a prognostic factor on the time-to-event through a hazard ratio. When the proportional hazards assumption is invalid, estimates based on the model are severely biased. The additive hazards model is accommodates time-varying effects in a natural way. In this project we develop methodology for additive hazards models based on maximum likelihood, including cross-validation and regularized estimates based on penalized likelihood.

Biobanks are collections of samples, data and images of individuals taken at different stages of their lives, either when they are ill or when they are healthy. They have agreed to take part in health-checks or population health studies. Biobanks are a vital source of information for fundamental and translational biomedical research aimed at the development of better predictive, preventive, personalized and participatory (‘4P’) health care. Historically, Dutch biobanks were developed independently, based on, for example, research interest, local or regional activities, or clinical discipline. Today, in the era of genomics and modern imaging, there is an urgent need to connect the extremely valuable information held in biobanks through a large-scale, standardized biobank infrastructure to avoid redundancy, create efficient research workflows, streamline and facilitate data access, and optimally link various sorts of data. Such a national biobank infrastructure will enable efficient health monitoring, validation of life style interventions and other ways to prevent disease, deliver better diagnostics and therapeutics, and expand the leading position of the Netherlands in international biomedical research. A large-scale biobank infrastructure is also essential to help generate the ‘evidence base’ required by registration and reimbursement authorities to assess the impact/quality/cost-benefit ratio of screening programmes, medications and treatments. The Netherlands is currently one of the world-leaders in biobanking with respect to both the number of its biobanks and the amount of material held by them, and in terms of its internationally knowledged expertise. In recent years, several important nationwide initiatives have been undertaken to organize and professionalize biobanking, including the String of Pearls Initiative (PSI), the European Population Imaging Infrastructure (EPI2), and the Translational Research IT project of the Centre for Translational Molecular Medicine (CTMM-TraIT). BBMRI-NL (Biobanking and BioMolecular Research Infrastructure Netherlands) was established to align, connect, complement and enrich biobanks, and to lay the groundwork for a robust national biobank facility. The first phase of BBMRI-NL (BBMRI-NL1.0; 2009-present) was highly successful and united 193 Dutch biobanks (population and clinical), jointly containing materials and data from >900,000 individuals, ~13 million biobanked samples, and a wide spectrum of accessory data. It lay the foundation for a well-organized national biobanking infrastructure for Dutch biomedical research. BBMRI-NL is the Dutch hub of the BBMRI ERIC (BBMRI European Research Infrastructure Consortium) and is closely aligned with major European initiatives and ‘Grand Challenges’ in the European Framework programme (e.g. Healthy Aging in Horizon 2020), and national initiatives, like the Life Sciences & Health ‘top sector’. With the recent complementary initiatives of EPI2 and CTMM-TraIT and major extra funding for data collection in specific sub-populations (notably the ‘Deltaplan Dementia’), the time has come to make the next decisive step towards a truly nationally integrated ‘NL Biobank Research Facility’ with BBMRI-NL2.0. In the future, biobanking will increasingly be integrated with health care. BBMRI-NL 2.0 will allow biomedical researchers to contribute to ‘4P’ medicine by linking diverse data sets through coordinated access to biomedical resources, technologies, standards and know-how, supported with cutting-edge IT systems and tools, and by providing tools for the standardization and harmonization of data, and its long-term storage. BBMRI-NL2.0 presents a unique opportunity to bring together Dutch biobank and imaging collections that are among the largest in Europe, building on proven success and extensive expertise in the compilation and analysis of such extensive datasets. BBMRI-NL2.0 will enable fully integrated access for research on how genetic and environmental factors contribute to disease. The research results from BBMRI-NL2.0 will drive new and improved ways to diagnose and predict disease, and highlight factors critical to disease prevention, healthy ageing or optimal development, and thus to the quality of life. It will cement the Netherlands’ leading position in biobank-based biomedical research. The envisioned NL-Biobank, with its content ranging from genes, molecules and images to their clinical cognate, will provide a unique repository of integrated data that optimally prepares the Netherlands for the challenges of Horizon 2020 and makes BBMRI-NL2.0 a highly visible and attractive partner for international collaborations.

In our NWO-Complexity project "Ecology meets human health" we investigated how an ecological view of human microbiota aids our understanding of these miniature ecosystems in relation to health and disease. We show how microbial interactions can be detected by patterns of interrelationship between bacteria. In applications to intestinal diseases, we show how new models and analytical techniques help us characterize disease and describe and predict individual response to treatment. In the future, these insights may lead to the development of practical microbiome-based interventions.