用于预测生物标志物增加到预后模型中的增量价值的微模拟模型。
Microsimulation model to predict incremental value of biomarkers added to prognostic models.
机构信息
Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, USA.
出版信息
J Am Med Inform Assoc. 2018 Oct 1;25(10):1382-1385. doi: 10.1093/jamia/ocy108.
It is unclear to what extent simulated versions of real data can be used to assess potential value of new biomarkers added to prognostic risk models. Using data on 4522 women and 3969 men who contributed information to the Framingham CVD risk prediction tool, we develop a simulation model that allows assessment of the added contribution of new biomarkers. The simulated model matches closely the one obtained using real data: discrimination area under the curve (AUC) on simulated vs actual data is 0.800 vs 0.799 in women and 0.778 vs 0.776 in men. Positive correlation with standard risk factors decreases the impact of new biomarkers (ΔAUC 0.002-0.024), but negative correlation leads to stronger effects (ΔAUC 0.026-0.101) than no correlation (ΔAUC 0.003-0.051). We suggest that researchers construct simulation models similar to the one proposed here before embarking on larger, expensive biomarker studies based on actual data.
目前尚不清楚模拟的真实数据在何种程度上可用于评估添加到预后风险模型中的新生物标志物的潜在价值。本研究利用弗雷明汉心血管疾病风险预测工具中 4522 名女性和 3969 名男性的信息，开发了一种模拟模型，可用于评估新生物标志物的附加贡献。模拟模型与使用真实数据获得的模型非常吻合：模拟数据和实际数据的判别曲线下面积（AUC）在女性中分别为 0.800 和 0.799，在男性中分别为 0.778 和 0.776。与标准风险因素的正相关性降低了新生物标志物的影响（AUC 差值为 0.002-0.024），但负相关性比无相关性（AUC 差值为 0.003-0.051）导致的影响更强（AUC 差值为 0.026-0.101）。我们建议研究人员在进行基于实际数据的更大、更昂贵的生物标志物研究之前，先构建类似于本文提出的模拟模型。
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