Keywords: optimization, robust optimization, screening policies
Major health decisions are often informed by mathematical models. Often multiple models study the same phenomenon, which may lead to conflicting decisions. We propose a framework for comparative modeling to identify decisions that perform well under all considered models. As example we use 3 models of prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that optimize quality-adjusted life expectancy. We use optimization to identify strategies that trade off between optimizing the average across all models’ assessments and being “conservative” by optimizing the most pessimistic model assessment. Among 10^6 practically implementable strategies, we identified 64 that trade off between maximizing the average and the most pessimistic model assessments. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision-makers’ conservativeness.