Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Dynamic treatment regimes are a statistical way of deciding what decisions to make. They originate in medicine, where tailoring treatments to individual patients - rather than prescribing the same drug to everyone with a particular disease - can lead to improved health. This is because the best treatment for two different patients may not be the same, such as older patients faring better than younger ones using a certain medication, or patients having varying degrees of tolerance to side-effects. By analyzing data we can find the best dynamic treatment regime for each patient: a set of rules that tell us the best treatment to prescribe at any given time based on an individual patient's characteristics. What's more, the potential of dynamic treatment regimes is not limited to medicine. In any situation where a sequence of decisions is made (such as steps in an engineering process, or decisions over time in a scientific experiment), we can analyze data to work out the best actions to take.

Within statistics, dynamic treatment regimes have received considerable study over the last decade, seeing the development of many different approaches to finding the best sequence of decisions in any given setting. This research program will expand this area of statistics into new, exciting directions. It focuses on solving problems that, though important, remain largely unaddressed in this relatively young field. For example, when analyzing data, specialists take a broad guess at how different decisions might affect an outcome, before statistical analyses 'fill in the blanks'. So far, relatively little work has been done in checking how good these broad guesses are. Another problem is that in the real world data are often measured with error (think how precisely you might measure a piece of wood, or the accuracy of your car's fuel gauge); this can badly undermine our results, so work needs to be done to take this problem into account.

This research will have impact within statistics as well as the wider research community. For the former, it will address important theoretical problems while forming new links between different areas of statistics (which in turn will advance the field as a whole). Beyond statistics, though seemingly specialized, this work has wide-reaching potential in decision-making processes. By rapidly expanding the contexts where dynamic treatment regimes can be applied, researchers from different specialties will find more opportunities to use - and benefit from - these methods. Finally, this program will see the training of numerous highly qualified individuals, who will go on to provide important contributions of their own, be it to statistics, the wider scientific community, industry, or Canada as a whole.