ArticleBiometrical journal. Biometrische Zeitschrift2020
Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.
Article in Biometrical journal. Biometrische Zeitschrift, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Developing more generalizable prediction models from pooled studies and large clustered data sets.Statistics in medicine · 2021Pooled it
- The Impact of the Number and the Size of Clusters on Prediction Performance of the Stratified and the Conditional Shared Gamma Frailty Cox Proportional Hazards Models.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Distance to junction and small saphenous vein treatment drive risk of ≥grade 2 endothermal heat-induced thrombosis in multicenter practice.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Evaluation of Women With Peripartum or Dilated Cardiomyopathy and Their First-Degree Relatives: The DCM Precision Medicine Study.Circulation. Genomic and precision medicine · 2026Observational
- Comparing methods for risk prediction of multicategory outcomes: dichotomized logistic regression vs. multinomial logit regression.BMC medical research methodology · 2024Article
- Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study.Frontiers in physiology · 2023Article
- Development and validation of prediction models for the discharge destination of elderly patients with aspiration pneumonia.PloS one · 2023Article
- Outcome prediction in newborn infants: Past, present, and future.Seminars in perinatology · 2022Review
- Article
- A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol.Diagnostic and prognostic research · 2021Article
- Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size.BMC medical research methodology · 2021Article
- Factors affecting the performance of brain arteriovenous malformation rupture prediction models.BMC medical informatics and decision making · 2021Article
- Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.Biometrical journal. Biometrische Zeitschrift · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Although multicenter data are common, many prediction model studies ignore this during model development. The objective of this study is to evaluate the predictive performance of regression methods for developing clinical risk prediction models using multicenter data, and provide guidelines for practice. We compared the predictive performance of standard logistic regression, generalized estimating equations, random intercept logistic regression, and fixed effects logistic regression. First, we presented a case study on the diagnosis of ovarian cancer. Subsequently, a simulation study investigated the performance of the different models as a function of the amount of clustering, development sample size, distribution of center-specific intercepts, the presence of a center-predictor interaction, and the presence of a dependency between center effects and predictors. The results showed that when sample sizes were sufficiently large, conditional models yielded calibrated predictions, whereas marginal models yielded miscalibrated predictions. Small sample sizes led to overfitting and unreliable predictions. This miscalibration was worse with more heavily clustered data. Calibration of random intercept logistic regression was better than that of standard logistic regression even when center-specific intercepts were not normally distributed, a center-predictor interaction was present, center effects and predictors were dependent, or when the model was applied in a new center. Therefore, to make reliable predictions in a specific center, we recommend random intercept logistic regression.
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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.