ArticleDiagnostic and prognostic research2022
Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation.
Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical prediction models for patients undergoing total hip arthroplasty: an external validation based on a systematic review and the Dutch Arthroplasty Register.Acta orthopaedica · 2024Pooled it
- Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data.BMC medical research methodology · 2024Article
- Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.The American statistician · 2024Article
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3 authors.
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Abstract
backgroundWhen a predictor variable is measured in similar ways at the derivation and validation setting of a prognostic prediction model, yet both differ from the intended use of the model in practice (i.e., "predictor measurement heterogeneity"), performance of the model at implementation needs to be inferred. This study proposed an analysis to quantify the impact of anticipated predictor measurement heterogeneity.
methodsA simulation study was conducted to assess the impact of predictor measurement heterogeneity across validation and implementation setting in time-to-event outcome data. The use of the quantitative prediction error analysis was illustrated using an example of predicting the 6-year risk of developing type 2 diabetes with heterogeneity in measurement of the predictor body mass index.
resultsIn the simulation study, calibration-in-the-large of prediction models was poor and overall accuracy was reduced in all scenarios of predictor measurement heterogeneity. Model discrimination decreased with increasing random predictor measurement heterogeneity.
conclusionsHeterogeneity of predictor measurements across settings of validation and implementation reduced predictive performance at implementation of prognostic models with a time-to-event outcome. When validating a prognostic model, the targeted clinical setting needs to be considered and analyses can be conducted to quantify the impact of anticipated predictor measurement heterogeneity on model performance at implementation.
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