OBJECTIVES: We compared 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. METHODS: We simulated prospective monitoring of a new drug (A) versus an alternative (B) with respect to two beneficial and three harmful outcomes.  We generated data for 1,000 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-MAR); INB with relative-value adjusted life years (INB-RVALY); and INB with quality-adjusted life years (INB-QALY).  We determined the proportion of iterations in which the 99% confidence interval (CI) for each metric included and excluded the null and we calculated mean time-to-alerting. RESULTS: With no true difference in any outcome between drugs A and B, the proportion of iterations including the null was lowest for INB-RVALY (64%) and highest for INB-QALY (76%).  When drug A was more effective and the drugs were equally safe, INB-QALY indicated net favorability of drug A in 81% of iterations, INB-MAR and INB-RVALY indicated net favorability in 79% of iterations, and NNT|NNH indicated net favorability in 72% of 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. CONCLUSIONS: BRA metrics can be useful for identifying net favorability when applied to prospective monitoring of a new drug versus an alternative.  INB-based approaches similarly outperform unweighted NNT|NNH approaches.