Modelling Individual Response to Treatment and Its Uncertainty:A Review of Statistical Methods and Challenges for Future Research

Abstract

Clinicians often have to make treatment decisions based on the likelihood that individual patients will receive benefit. This information has to be derived from appropriate studies and provided in a way that easily transforms from the statistical model into clinical practice. This chapter discusses four methodological strategies to provide information for treatment decisions: subgroup analysis, regression models, models for potential outcome, and prediction models. Based on how statistical techniques are able to provide information for stratified, precision, or individualized medicine we present the challenges that exist in analysis, reporting and applying those results. We also identify relevant ethical issues. There is still a substantial gap between formal statistical results on patient treatment interaction and ways in which they can be presented to clinicians as easy to use tools in clinical decision-making. At the moment, most energy is spent in the discovery of biomarkers which help to grasp patient treatment interaction. Less energy is spent in corresponding validation studies and the process of translating statistical results into clinical practice. Furthermore, clinical studies that provide evidence for successful translation of predictive biomarkers into clinical practice are needed.