Abstract
Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.