Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets
To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predi...
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| Vydáno v: | JOURNAL OF CLINICAL EPIDEMIOLOGY Ročník 137; s. 83 - 91 |
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| Médium: | Journal Article Publikace |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.09.2021
Elsevier Limited |
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| ISSN: | 0895-4356, 1878-5921, 1878-5921 |
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| Abstract | To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.
We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models’ generalizability across the included general practices.
Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke.
In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies. |
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| AbstractList | AbstractObjectiveTo illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. Study Design and SettingWe developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models’ generalizability across the included general practices. ResultsAmong 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. ConclusionIn large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies. To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models’ generalizability across the included general practices. Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies. ObjectiveTo illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.Study Design and SettingWe developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models’ generalizability across the included general practices.ResultsAmong 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke.ConclusionIn large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies. To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.OBJECTIVETo illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices.STUDY DESIGN AND SETTINGWe developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices.Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke.RESULTSAmong 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke.In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies.CONCLUSIONIn large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies. |
| Author | Nijman, Steven Debray, Thomas P.A. Takada, Toshihiko Snell, Kym I.E. Denaxas, Spiros Nguyen, Tri-Long Uijl, Alicia Asselbergs, Folkert W. |
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| Copyright | 2021 The Authors Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. 2021. The Authors |
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| DOI | 10.1016/j.jclinepi.2021.03.025 |
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| Snippet | To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.
We developed eight Cox... AbstractObjectiveTo illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. Study... ObjectiveTo illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.Study Design... To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.OBJECTIVETo illustrate... |
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| SubjectTerms | Body mass index Calibration Cluster Analysis Congestive heart failure Datasets Datasets as Topic - statistics & numerical data Discrimination Electronic health records Epidemiology Ethnicity Forecasting Heterogeneity Humans Internal Medicine Maximum likelihood estimation Model comparison Models, Statistical Population Prediction model Prediction models Primary care Regression analysis Regression models Statistical analysis Validation Variables |
| Title | Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets |
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