Evaluating the Impact of Uncertainty on Risk Prediction: Towards More Robust Prediction Models.

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. PMID: 30815191 [PubMed - indexed for MEDLINE]
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research