Better methods, better scienceThis research group aims to continuously improve the quality of medical research, by applying, teaching and developing statistical methods and statistical thinking, aiming for broad use by researchers.
Kit Roes explains
"The importance of statistics and statistical thinking in medical research continues to grow. Innovation and personalization of medical practice is increasingly data driven. Innovations need to find their way to application faster, without compromising the strength of evidence."read interview
Kit Roes explains
"Medical researchers are continually gathering data in order to answer their research questions. Which treatment is best? Can we predict how well patients will respond to therapy? Which gene lies at the root of a hereditary disease? Statistics is essential to answer such questions. Not only does it allow researchers to draw reliable conclusions from clinical studies and biological experiments, it also makes clear how certain we can be about these conclusions."
Q1 What is the aim of the Biostatistics group?
"The Biostatistics group strives to improve the quality of statistical methods that are used by researchers throughout Radboudumc and beyond. We give advice to researchers on the most efficient design of studies, on the most effective methods for analysis, and on the correct interpretation of the results. Moreover, we are very active in the development of novel statistical methods, in order to have an impact on healthcare research internationally."
Q2 Why is statistics important?
"The importance of statistics and statistical thinking in medical research continues to grow. Innovation and personalization of medical practice is increasingly data driven. Innovations need to find their way to application faster, without compromising the strength of evidence. This requires smart research designs and advanced statistical analyses to assess the effectiveness of therapies. Modern technology allows more and more complex measurements to be gathered for patients, for example using DNA sequencing, fMRI, or continuous monitoring through health-related apps. Research questions, for example in the area of personalized medicine, are increasingly sophisticated. Scientific journals and regulators also set progressively higher standards for the quality of data analysis. Taken all together it is clear that statisticians have a key role to play in almost every branch of medical research as well continuously need to educate researchers in this field."
Q3 Can you tell us a bit about your methods?
"In our statistical research we develop novel approaches to design studies (such as sequential or adaptive designs; or designs that use prior information effectively) and methods to solve data analysis problems we encounter or foresee, given new research questions and types of data. Such methods are typically motivated by practical needs of researchers, but also of sufficient theoretical generality to be valuable for other, related application areas. The methods we have developed are frequently applied in many fields within medicine and elsewhere. The research of the Biostatistics group is concentrated on methods for new trial designs and personalized healthcare."
Q4 Who do you cooperate with?
"Close cooperation with other research groups is key to the success of our efforts, which always takes place in a strongly multidisciplinary environment, and we collaborate with investigators from all over Radboudumc. On the one hand our mathematical and statistical expertise is essential to solve problems in medical research. On the other hand, our own methodological research benefits greatly from our efforts in applying statistics in medicine, because it keeps us focused on solving statistical problems that applied researchers face in practice. In addition, we have a strong national and international collaborative network with medical statistics groups.”
Q5 What is statistics about?
"Statistics is at the heart of the scientific method. It provides the language and the methods to design, analyse and interpret quantitative research, and allows us to quantify how uncertain we are of conclusions. It is about drawing the line between what we can confidently conclude from an experiment, and what we have to remain unsure about. Improving the quality of statistical methods means improving the quality of science."