ArticleStatistics in medicine2017
Simpson's paradox in the integrated discrimination improvement.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Prognostic Immune and Nutritional Index as a Predictor of Survival in Patients Undergoing Curative-Intent Resection for Gastric Cancer.Medicina (Kaunas, Lithuania) · 2025Article
- A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).La Radiologia medica · 2023Article
- Development and Validation of a Nomogram Based on Nutritional Indicators and Tumor Markers for Prognosis Prediction of Pancreatic Ductal Adenocarcinoma.Frontiers in oncology · 2021Article
- HIV Viremia Is Associated WithFrontiers in medicine · 2021Article
- Quantitative Genomic Dissection of Soybean Yield Components.G3 (Bethesda, Md.) · 2020Article
- Construction of a MicroRNA-Based Nomogram for Prediction of Lung Metastasis in Breast Cancer Patients.Frontiers in genetics · 2020Article
- First things first: risk model performance metrics should reflect the clinical application.Statistics in medicine · 2017Article
- Clinical risk reclassification at 10 years.Statistics in medicine · 2017Review
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Abstract
The integrated discrimination improvement (IDI) is commonly used to compare two risk prediction models; it summarizes the extent a new model increases risk in events and decreases risk in non-events. The IDI averages risks across events and non-events and is therefore susceptible to Simpson's paradox. In some settings, adding a predictive covariate to a well calibrated model results in an overall negative (positive) IDI. However, if stratified by that same covariate, the strata-specific IDIs are positive (negative). Meanwhile, the calibration (observed to expected ratio and Hosmer-Lemeshow Goodness of Fit Test), area under the receiver operating characteristic curve, and Brier score improve overall and by stratum. We ran extensive simulations to investigate the impact of an imbalanced covariate upon metrics (IDI, area under the receiver operating characteristic curve, Brier score, and R
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Registered trials
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.