Petousis Panayiotis, Naeim Arash, Mosleh Ali, Hsu William
Medical Imaging & Informatics, Department of Radiological Sciences and Bioengineering.
Department of Medicine, David Geffen School of Medicine.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1461-1470. eCollection 2018.
Risk prediction models are crucial for assessing the pretest probability of cancer and are applied to stratify patient management strategies. These models are frequently based on multivariate regression analysis, requiring that all risk factors be specified, and do not convey the confidence in their predictions. We present a framework for uncertainty analysis that accounts for variability in input values. Uncertain or missing values are replaced with a range of plausible values. These ranges are used to compute individualized risk confidence intervals. We demonstrate our approach using the Gail model to evaluate the impact of uncertainty on management decisions. Up to 13% of cases (uncertain) had a risk interval that falls within the decision threshold (e.g., 1.67% 5-year absolute risk). A small number of cases changed from low- to high-risk when missing values were present. Our analysis underscores the need for better communication of input assumptions that influence the resulting predictions.
风险预测模型对于评估癌症的检测前概率至关重要，并应用于对患者管理策略进行分层。这些模型通常基于多变量回归分析，要求指定所有风险因素，并且无法传达其预测的置信度。我们提出了一个不确定性分析框架，该框架考虑了输入值的变异性。不确定或缺失的值将被一系列合理的值所取代。这些范围用于计算个体化风险置信区间。我们使用盖尔模型展示了我们的方法，以评估不确定性对管理决策的影响。高达13%的病例（不确定）的风险区间落在决策阈值内（例如，5年绝对风险为1.67%）。当存在缺失值时，少数病例从低风险变为高风险。我们的分析强调了更好地传达影响最终预测的输入假设的必要性。
