ArticleStatistical methods in medical research2019
Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Accommodating population differences when validating risk prediction models.Statistics in medicine · 2022Article
- Personalized antibiograms for machine learning driven antibiotic selection.Communications medicine · 2022Article
- Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer.Breast cancer research : BCR · 2021Article
- Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020Article
- Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results.Journal of the National Cancer Institute · 2020Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.