To compare benefit-risk assessment (BRA) methods for determining whether and when sufficient evidence exists to indicate that one drug is favorable over another in prospective monitoring.
We simulated prospective monitoring of a new drug (A) versus an alternative drug (B) with respect to two beneficial and three harmful outcomes. We generated data for 1000 iterations of six scenarios and applied four BRA metrics: number needed to treat and number needed to harm (NNT|NNH), incremental net benefit (INB) with maximum acceptable risk, INB with relative-value–adjusted life-years, and INB with quality-adjusted life-years. We determined the proportion of iterations in which the 99% confidence interval for each metric included and excluded the null and we calculated mean time to alerting.
With no true difference in any outcome between drugs A and B, the proportion of iterations including the null was lowest for INB with relative-value–adjusted life-years (64%) and highest for INB with quality-adjusted life-years (76%). When drug A was more effective and the drugs were equally safe, all metrics indicated net favorability of A in more than 70% of the iterations. When drug A was safer than drug B, NNT|NNH had the highest proportion of iterations indicating net favorability of drug A (65%). Mean time to alerting was similar among methods across the six scenarios.
BRA metrics can be useful for identifying net favorability when applied to prospective monitoring of a new drug versus an alternative drug. INB-based approaches similarly outperform unweighted NNT|NNH approaches. Time to alerting was similar across approaches.