Prognosis research strategy (PROGRESS) 4: stratified medicine research.
What is stratified medicine? Stratified medicine refers to the targetting of treatments (including pharmacological and non-pharmacological interventions) according to the biological or risk characteristics shared by subgroups of patients. Stratified medicine is regarded as central to the progress of healthcare according to the leaders of the National Institutes of Health, and the Food and Drug Administration6 among others.7 In contrast with “all comer” or “empirical” medicine, stratified medicine seeks to target therapy and make the best decisions for groups of similar patients.8 9 One approach to stratifying the use of treatments is to consider absolute risks. In the third article of our series10 we described how prognostic models are used to estimate the absolute risk of an outcome for an individual. Those people with the highest absolute risk will derive the largest absolute benefit from a treatment (that is, the greatest reduction in probability of the outcome) when the treatment effect expressed in relative terms is the same for all patients. This is illustrated in the upper panel of fig 2⇓, where the relative treatment effect on mortality risk is estimated as 0.75 for all patients but the reduction in absolute probability of death is 5% for low risk patients and 15% for high risk patients. In such situations treatments could be restricted (or “personalised”) to those who will benefit the most. Examples in common clinical practice include the decision to give lipid lowering therapy to people above a certain threshold of cardiovascular risk estimated from a prognostic model,11 the use of bisphosphonates for women over the age of 50 considered to have an increased risk of vertebral fractures, and the targeting of primary care management of back pain.12 View larger version: In this window In a new window Fig 2 Estimated treatment effect in subgroups defined according to (upper panel) risk from a prognostic model and (lower panel) a factor that predicts differential treatment response. The prevalence of positive factor and high risk is shown, arbitrarily, as 20%. The dotted vertical line shows the overall treatment effect, the centre of each box shows the effect estimate, and the horizontal lines show confidence intervals By contrast, clinicians may also stratify medicine because the relative treatment effect is inconsistent across patients (fig 2, lower panel⇑). In this situation, at least one individual patient measure is associated with changes in the treatment effect. In statistical terms there is an interaction between a patient-level variable and the effect of treatment on the outcome, and in biological terms there may be an underlying mechanism explaining the interaction. In this situation, a stratified medicine approach seeks to test patients for the presence of individual factors that are considered predictive of an improved treatment response (more benefit, less harm, or both), as in the aforementioned test for positive HER-2 status in breast cancer and the use of trastuzumab. Other examples in clinical use include imatinib in patients with chronic myeloid leukaemia targeted to those with the BCR-ABL mutation13 and gefitinib used to treat pulmonary adenocarcinoma in patients with epidermal growth factor receptor mutations.14 An example of identifying patients with greater risk of harms include the antiretroviral drug abacavir,15 where HLA typing helps identify patients at high risk of abacavir toxicity. Thus a key part of stratified medicine research is to identify suitable tests for predicting treatment response from specific interventions. The use of HER-2 status in breast cancer management illustrates how tests of differential treatment response are often thought of as binary factors: a biomarker is classed as positive or negative, or laboratory values are deemed low or high. Such dichotomisation facilitates clinical decision making and is used in most examples described in this paper. However, many tests have original values measured on an ordinal or a continuous scale. Similarly if prognostic models10 are considered as tests, they usually produce a continuous risk score for each individual; the same applies to gene signatures or related indices derived from high dimensional data. Statistically, there is more power and less potential for bias if such tests are evaluated on their original scale (see later) rather than being dichotomised by means of a cut point10; categorisation may then be done after analysis to aid clinical strategies. For example, Flynn et al derived a prognostic model to identify patients with back pain who would respond well to manipulation rather than to other types of treatment such as exercise.16 Some trials randomising patients to these treatments found that patients with positive scores from the model had greater relative and absolute benefits from manipulation than those with negative scores.17 18 Thus stratified medicine uses baseline information about a patient’s likely response to treatment to tailor treatment decisions. This is different from stepped19 or adaptive20 models of care in which tailoring of treatment depends on the patient’s actual response to previously offered treatment, with a sequence of interventions (which may differ in intensity, duration, cost, or complexity) being offered to those who have not responded sufficiently. Our focus here, though, is on the initial stratification of treatment based on the predicted (rather than actual) response to treatment.