Optimizing medical decision making: advancing and refining estimation and evaluation of personalized treatment rules

Erin Gabriel, University of Copenhagen
Grant amount: DKK 7,618,938

The project aims to develop statistical methods to improve personalized treatment decision-making while considering patient burden and accounting for the shortcomings of the data being used.

As medical data and treatment options grow, a vital question becomes how to use the information available to make the best treatment decision. Methods exist to help select the best treatment for each patient based on patient and disease characteristics. However, these methods often do not consider the patient’s burden for collecting those characteristics, nor do they account for the potential shortcomings of the data used in the selection. Both issues can lead to the selection of sub-optimal treatments and potential harm to patients. To avoid this, selected decisions should be tested in clinical trials, and the patient burden should always be considered. Randomized clinical trials can be costly, untimely, or simply impossible. The use of validated surrogate endpoints can make randomized clinical trials feasible, but in the setting of personalized treatment, improved statistical evaluation methods are needed. Regardless of the data collection type, there is also a need for statistical tools that account for patient burden in treatment selection. Finally, when observational data must be used, improved methods are needed for treatment selection that account for biases that may occur due to the lack of randomization.

Project participants
Erin Gabriel, Associate Professor
University of Copenhagen, Department of Public Health