A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes

• Published in: Computer methods and programs in biomedicine Vol. 204; p. 106073
• Main Authors: Diaz-Ramirez, L. Grisell, Lee, Sei J., Smith, Alexander K., Gan, Siqi, Boscardin, W. John
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2021
• Subjects: Aged; Algorithms; backward elimination; Bayes Theorem; Bayesian Information Criterion; Humans; Prognosis; prognostic models; survival analysis; variable selection; prognostic models; Bayesian Information Criterion; variable selection; survival analysis; backward elimination
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2021.106073

•It is possible to select a common set of variables to predict multiple clinical outcomes while maintaining parsimony and predictive accuracy.•We extended the use of common variable-selection methods—like backward elimination and criterion-based model selection—for single outcomes to the prediction of multiple clinical outcomes.•Researchers will be able to use our algorithm and code to build prognostic models that are both accurate and parsimonious, potentially saving the clinical time and expense associated with gathering additional unnecessary predictors. Most 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. Our 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. In 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. Our method identified a common subset of variables to predict multiple clinical outcomes with superior balance between parsimony and predictive accuracy to current methods.