Background: Risk reclassification tables are an effective means of evaluating the added prognostic information provided by a new risk marker beyond an established set of risk markers. These cross-tabulations display the allocation of individuals into clinicallyrelevant risk strata both before and after the addition of a new risk marker into the risk estimation process. Risk reclassification tables have been lauded as providing a better metric of the clinical utility of a new risk marker than traditional metrics such as p-values, effect sizes, and c-statistics, as the extent of reclassification reveals how often therapeutic anagement may change when incorporating the new risk marker into risk estimation, and thus, therapeutic decision making. The ultimate clinical utility of new risk markers would be best judged within the context of randomized clinical trials. Unfortunately, given the anticipated small clinical effects of a marker-guided treatment strategy, and a large and ever-increasing number of markers requiring evaluation, such trials would necessarily be large, numerous, costly, and unlikely to ever be performed. Thus, novel methods for evaluating the potential impact of marker-guided therapeutic strategies on clinical outcomes should be sought.
Methods: Accordingly, the current report describes a quantitative method utilizing risk reclassification tables which enables estimating the effect of incorporating a new risk marker into therapeutic decisions on clinical outcomes. The proposed method employs (1) an appropriate risk reclassification table, (2) estimated event rates within risk strata, and (3) estimated treatment effects within risk strata to estimate the relative and absolute decrease in the expected number of events when incorporating a new risk marker into therapeutic decisions via a risk reclassification strategy. A real-life example demonstrates the approach.
Conclusions: The current report describes a straightforward method for indirectly estimating the potential clinical effect of incorporating a new risk marker into therapeutic decision making via a risk reclassification strategy. In the absence of randomized trials directly estimating the effect of a marker-guided strategy on clinical outcomes, the current method enables an indirect approach for anticipating the outcome of such trials.
Keywords: Risk, reclassification table, clinical prediction model, biomarker