Denis Talbot

Denis Talbot, PhD, is a regular researcher at the CHU de Québec – Université Laval Research Center, and a full professor of biostatistics in the Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval. He holds a doctorate in mathematics with a concentration in statistics from the Université du Québec à Montréal, and completed a postdoctoral fellowship in biostatistics at the University of Washington.

Professor Talbot’s research program focuses on statistical methods for analyzing data to facilitate causal inference. Causal inference involves predicting the effect of an intervention using data. When these data come from an observational study, that is, one in which exposure and health outcome are observed without intervention by the researcher, different factors may be associated with both exposure and response, creating a misleading association between exposure and outcome, known as confounding bias. To obtain valid results, it is therefore necessary to use data analysis methods that minimize confounding bias. Through his research program, Professor Talbot evaluates, compares, and develops new data analysis methods to minimize confounding bias. To do this, he notably uses mathematics and artificial intelligence.

One of his current research themes is the analysis of medico-administrative data, for example to evaluate the effectiveness of drugs in real-life situations, or to rapidly monitor the effectiveness of vaccines. These data offer many advantages, including a wealth of information on a large number of individuals. However, they also present particular challenges because of their very large size, and because they are not initially collected for research purposes. Developing analysis methods adapted to this type of data will maximize the benefits of their use and minimize the drawbacks.

Another of Professor Talbot’s research themes concerns personalized medicine methods. These methods seek to determine the best medical treatment for a given individual at a given time, considering their characteristics. One of Professor Talbot’s interests is the use of artificial intelligence in this context to reduce confounding biases and identify new characteristics that are useful to take into account when personalizing treatments.

As an expert in statistical methods, Professor Talbot collaborates on research projects in a wide range of fields. In recent years, he has collaborated on research projects concerning occupational health, cardiovascular health, mental health, breast cancer and vaccine effectiveness, among others.

In summary, Professor Talbot’s research program contributes to developing new data analysis methods. These methods will better equip medical researchers to analyze their data.