Assessing the impact of biomarkers on patient outcome: an obligatory step.

Abstract

Payers for healthcare increasingly require evidence about health outcomes of medical interventions. Outcomes research uses various study designs to provide such evidence, with the highest level of evidence provided by randomized controlled trials (RCTs). Among published studies of biomarkers, however, relatively few determine the relationship of biomarker testing to outcomes, and only a small fraction of those studies are RCTs, and fewer still follow the CONSORT standards for reporting of trials. Outcomes studies of biomarkers are difficult to carry out. During an outcomes study, clinicians may be expected to use the results of the test (e.g., troponin) along with other information (e.g., symptoms of an acute coronary syndrome) to decide about use of another intervention (such as cardiac catheterization) that is hoped to improve an outcome (e.g., mortality rate) at some time in the future. Studies of diagnostic tests frequently lack evidence that test results were acted upon at all, much less according to a defined protocol. The potential for a biomarker to improve outcomes depends upon a wide range of variables. These variables include the diagnostic accuracy of the test and the effectiveness of the therapeutic intervention, both of which will, predictably, vary with the patient population studied. Thus outcomes studies performed in one patient population leave unanswered questions regarding outcomes in other populations. The questions are infinite, but resources are finite. Simulation modelling studies are attractive as an adjunct to patient studies to address multiple patient variables and multiple treatment approaches without the expense of multiple clinical studies.