Treatment recommendations based on Network Meta-Analysis: rules for risk-averse decision-makers

Abstract

ABSTRACTBackgroundThe treatment recommendation based on a Network Meta-analysis (NMA) is usually the single treatment with the highest Expected Value (EV) on an evaluative function. We explore approaches which recommend multiple treatments and which penalize uncertainty, making them suitable for risk-averse decision makers.MethodsWe introduce Loss-adjusted EV (LaEV) and compare it to GRADE and three probability-based rankings. We define the properties of a valid ranking under uncertainty and other desirable properties of ranking systems. A two-stage process is proposed: the first selects treatments superior to the reference treatment; the second identifies those that are also within a Minimal Clinically Important Difference (MCID) of the best treatment. Decision rules and ranking systems are compared on stylized examples and 10 NMAs used in NICE Guidelines.ResultsOnly LaEV reliably delivers valid rankings under uncertainty and has all the desirable properties. In 10 NMAs comparing between 4 and 40 treatments, an EV decision maker would recommend 4-14 treatments, and LaEV 0-3 (median 2) fewer. GRADE rules give rise to anomalies, and, like the probability-based rankings, the number of treatments recommended depends on arbitrary probability cutoffs. Among treatments that are superior to the reference, GRADE privileges the more uncertain ones, and in 3/10 cases GRADE failed to recommend the treatment with the highest EV and LaEV.ConclusionsA two-stage approach based on MCID ensures that EV- and LaEV-based rules recommend a clinically appropriate number of treatments. For a risk-averse decision maker, LaEV is conservative, simple to implement, and has an independent theoretical foundation.HighlightsWhat is already known?A risk-neutral decision-maker should make treatment decisions based on Expected Value (EV), meaning that the single treatment with the highest expected efficacy from a network meta-analysis should be recommended, regardless of uncertainty. In practice, decision makers may recommend several treatments, and take uncertainty into account on anad hocbasis.What is new?We introduce Loss-adjusted EV (LaEV) as a mechanism for risk-averse decision making, and set out desirable properties of ranking systems. We define a ranking as valid under uncertainty if a higher EV is ranked above a lower one at the same uncertainty and a lower uncertainty above a higher one at the same EV. We compare LaEV to GRADE and probabilistic rankings. Of the methods examined, only LaEV provides a valid ranking under uncertainty and has all the desirable properties.ImplicationsFor a risk-averse decision maker, LaEV is a reliable, conservative, and easy-to-implement decision metric, with an independent theoretical foundation. Adoption of a risk-averse stance might focus attention on more accurate quantification of uncertainty, and encourage generation of better quality evidence.