Keywords: Value of Information, Effect Modification, Randomized Control Trials, Heart Failure, Medical Device Efficacy
When treatment response is uncertain, identifying patients that are likely to receive the most benefit is crucial. Factors that influence the expected outcomes of treatment are called effect modifiers. In many cases, several possible effect modifiers require more study before they can be incorporated into clinical guidance for targeting treatment. To formally determine research priorities for further study of potential treatment effect modifiers, we apply a value of information framework. We quantify the value of investing in a future randomized control trial (RCT) powered to estimate outcomes of treatment in a subpopulation defined by a potential effect modifier. Specifically, we consider effect modifiers of device-based treatments for heart failure. Existing evidence on modifiers of mortality and hospitalization risk reduction includes both well-powered RCTs and observational data. We provide general conclusions for choosing candidate effect modifiers for future research, with inputs that include population prevalence, the underlying true magnitude of effect modification, and the current strength of existing evidence.