Prognosis research strategy (PROGRESS) 4: Stratified medicine research

The PROGRESS series (www.progress-partnership.org) sets out a framework of four interlinked prognosis research themes and provides examples from several disease fields to show why evidence from prognosis research is crucial to inform all points in the translation of biomedical and health related research into better patient outcomes. Recommendations are made in each of the four papers to improve current research standards What is prognosis research? Prognosis research seeks to understand and improve future outcomes in people with a given disease or health condition. However, there is increasing evidence that prognosis research standards need to be improved Why is prognosis research important? More people now live with disease and conditions that impair health than at any other time in history; prognosis research provides crucial evidence for translating findings from the laboratory to humans, and from clinical research to clinical practice Stratified medicine involves tailoring therapeutic decisions for specific, often biologically distinct, individuals, the aim being to maximise benefit and reduce harm from treatment, or to rescue a treatment that fails to show overall benefit in unselected patients but does benefit specific patients Stratified medicine can use absolute risks. When a treatment effect measured on a relative scale (such as relative risk) is the same for all patients, those with the highest absolute risk will derive the largest absolute benefit from the treatment When the relative treatment effect is inconsistent across patients, stratified medicine can use tests which measure factors (such as biomarker levels or genotypes) that predict individual treatment response. However, the clinical use of such tests is currently small, and rigorous evidence of impact is sometimes lacking, with flaws in study design, analysis, and reporting leading to potentially spurious evidence either for or against a factor Research to identify factors that truly predict treatment effect could be improved by: Labelling exploratory analyses as exploratory, to minimise false positive findings Increasing statistical power by designing trials with adequate sample sizes, facilitating collaborations across research groups and meta-analyses of individual participant data from multiple trials, and by analysing continuous factors on their original scale Estimating, for a truly binary factor, the difference in relative treatment effect between positive and negative groups within randomised trials that include both factor positive and factor negative patients in both control and treatment groups Considering biological or other mechanisms for modification of treatment response, either to motivate new research or to support statistical evidence that a factor interacts with treatment Prognosis research in general should play a more central role in stratified medicine research: from identifying conditions with clinically important differences in absolute risk of outcome across patients, to identifying factors that predict individual treatment response, and to examining the cost and impact of implementing stratified medicine approaches in practice.