Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence.

Although often conflated, determining the best treatment for an individual (the task of a doctor) is fundamentally different from determining the average effect of treatment in a population (the purpose of a trial). In this paper, we review concepts of heterogeneity of treatment effects (HTE) essential in providing the evidence base for precision medicine and patient-centred care, and explore some inherent limitations of using group data (e.g. from a randomized trial) to guide treatment decisions for individuals. We distinguish between person-level HTE (i.e. that individuals experience different effects from a treatment) and group-level HTE (i.e. that subgroups have different average treatment effects), and discuss the reference class problem, engendered by the large number of potentially informative subgroupings of a study population (each of which may lead to applying a different estimated effect to the same patient), and the scale dependence of group-level HTE. We also review the limitations of conventional 'one-variable-at-a-time' subgroup analyses and discuss the potential benefits of using more comprehensive subgrouping schemes that incorporate information on multiple variables, such as those based on predicted outcome risk. Understanding the conceptual underpinnings of HTE is critical for understanding how studies can be designed, analysed, and interpreted to better inform individualized clinical decisions.