ArticleComputer methods and programs in biomedicine2021
A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- External validation and comparison of five prediction models for lymph node metastasis after noncurative endoscopic submucosal dissection for early gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Development and validation of a clinical nomogram for predicting cumulative live birth rate in ovarian endometrioma patients undergoing ethanol sclerotherapy andFrontiers in endocrinology · 2026Article
- Uncertainty quantification of the pressure waveform using a Windkessel model.International journal for numerical methods in biomedical engineering · 2024Article
- A comprehensive prognostic tool for older adults: Predicting death, ADL disability, and walking disability simultaneously.Journal of the American Geriatrics Society · 2022Article
- A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes.Computer methods and programs in biomedicine · 2021Article
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5 authors.
Funding
Abstract
BACKGROUND AND
objectiveMost methods for developing clinical prognostic models focus on identifying parsimonious and accurate models to predict a single outcome; however, patients and providers often want to predict multiple outcomes simultaneously. As an example, for older adults one is often interested in predicting nursing home admission as well as mortality. We propose and evaluate a novel predictor-selection computing method for multiple outcomes and provide the code for its implementation.
methodsOur proposed algorithm selected the best subset of common predictors based on the minimum average normalized Bayesian Information Criterion (BIC) across outcomes: the Best Average BIC (baBIC) method. We compared the predictive accuracy (Harrell's C-statistic) and parsimony (number of predictors) of the model obtained using the baBIC method with: 1) a subset of common predictors obtained from the union of optimal models for each outcome (Union method), 2) a subset obtained from the intersection of optimal models for each outcome (Intersection method), and 3) a model with no variable selection (Full method). We used a case-study data from the Health and Retirement Study (HRS) to demonstrate our method and conducted a simulation study to investigate performance.
resultsIn the case-study data and simulations, the average Harrell's C-statistics across outcomes of the models obtained with the baBIC and Union methods were comparable. Despite the similar discrimination, the baBIC method produced more parsimonious models than the Union method. In contrast, the models selected with the Intersection method were the most parsimonious, but with worst predictive accuracy, and the opposite was true in the Full method. In the simulations, the baBIC method performed well by identifying many of the predictors selected in the baBIC model of the case-study data most of the time and excluding those not selected in the majority of the simulations.
conclusionsOur method identified a common subset of variables to predict multiple clinical outcomes with superior balance between parsimony and predictive accuracy to current methods.
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