Methodological research

The section conducts biostatistical research and development, participates in substantive medical research projects and offers biostatistical advice to PhD students and staff in all disciplines of the Faculty of Health Sciences, to those engaged in experimental and clinical medicine, public health and odontology.

Here are some common research themes, expand the accordions to view more information and faculty members who are engaged in research on that theme. 


Censored survival data occur when the exact survival time is known only for some individuals in a study, whereas for the rest of the individuals, it is only known that they were still alive at some time. The presence of censoring has consequences for how descriptive and inferential statistics should be performed. 

Statistics methods for the analysis of event history data has been a major research topic for the Section of Biostatistics since it was established in 1978. Members of the section have been influential in the development of the mathematical statistical properties and applications of methods in event history analysis. Aside from the numerous manuscripts on the topic, part of the work in this area has been summarized in the book Statistical Models Based on Counting Processes, published in 1993 by PK Andersen, Ø Borgan, RD Gill, and N Keiding. Further developments have been published in the book Dynamic Regression Models for Survival Data, published in 2006 by T Martinussen and TH Scheike.

Our faculty have developed or contributed to statistical software for event history analysis that is used by people all over the world. Some of the R packages developed at our section include:  mets, prodlim, eventglm, riskRegression, and timereg

Faculty working in this area

PK Andersen, PF Blanche, F Eriksson, TA Gerds, T Martinussen, B Ozenne, H Rytgaard, T Scheike



The field of causal inference deals with the challenges of inferring cause and effect (for example the effect of a new treatment or intervention) from data. This includes defining formal mathematical frameworks for causation, creating methods for inferring causal structures using data, developing efficient estimators for causal effects, and deriving bounds or sensitivity analyses for causal effects that are robust to certain assumptions. 

Our faculty have developed or contributed to statistical software for causal inference that is used widely. Some of the R packages developed at our section include: causaloptim, causalDisco, and pseval

Faculty working in this area

EE Gabriel, TA GerdsA Jensen, T Martinussen, AH Petersen, H Rytgaard, MC Sachs 


Statistical accuracy measures such as Receiver Operating Characteristics (ROC) and utility functions like the Brier score are important tools for developing diagnostic and predictive models. The Section is frequently involved in applied projects where the aim is to predict the current status (diagnosis) or the future status (prediction) of new patients based on data. The Section is actively involved in the development of theory, methods, and computational tools for medical decision theory, including machine learning. Part of this work has been published in the book Medical Risk Prediction Models With Ties to Machine Learning by TA Gerds, M Kattan.

Our faculty have developed or contributed to statistical software for prediction modeling in the statistical software R. Some of the R packages developed at our Section include: plotROC, timeROC, wally, and riskRegression.

Faculty working in this area

PF Blanche, EE Gabriel, TA Gerds, J Hilden, MC Sachs



Statistical genetics is a multidisciplinary field that aims to understand the genetic basis of traits and diseases. Faculty in the Section play a vital role in developing statistical methods, analyzing genetic data, estimating heritability, investigating gene-environment interactions, and collaborating with other researchers to interpret the findings. Their contributions help advance our understanding of genetic influences on human health and complex traits. The methods and tools developed are applicable beyond genetics to all sorts of modern, high-dimensional -omics data.

Faculty working in this area

CT Ekstrøm, AK Jensen, A Meddis, T Scheike





Randomized controlled trials (RCTs) are powerful research designs used to evaluate the effectiveness and impact of interventions or treatments in medicine and public health. They are considered the gold standard for establishing causal relationships between an intervention and its outcomes. Faculty and staff at the Section play a critical role throughout the entire RCT process, from study design to data analysis and interpretation. Their expertise helps ensure the rigor, reliability, and validity of the trial, providing valuable evidence to inform decision-making and improve the quality of interventions in various medical research areas. They also develop theory and methods to improve the efficiency and reliability of RCTs.

Faculty working in this area

P Blanche, EE Gabriel, AKG Jensen, AK Jensen



This research theme deals with the statistical aspects of the theory and techniques of measurement. Measurement of health, states of mind, and other aspects of the human condition are crucial to the use of data in science. 

Statistical methods for the theory of response and measurement has a long history in Denmark. George Rasch (1901 - 1980) was a Danish mathematician and statistician who developed the Rasch model. Members of the Section have studied that and related models for many years. Some of this work is summarized in the book Rasch Models in Health, published in 2012 and edited by KB Christensen, S Kreiner, and M Mesbah. 

The standalone statistical software package DIGRAM for discrete graphical models was developed at the Section. 

Faculty working in this area

KB Christensen, EB JørgensenS KreinerJH Petersen



Student research

The Section frequently has research opportunities for students at the bachelors, masters, and doctoral levels. Students at the Section work closely with the faculty to develop, evaluate, and apply novel methods and tools in biostatistics. Contact a researcher directly for more information.