ArticleStatistics in medicine2023
Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Comparison of Methods for Incorporating Related Data When Developing Clinical Prediction Models: A Simulation Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Development and temporal validation of five year retinal arteriosclerosis risk prediction in high risk adults from an East China cohort.Scientific reports · 2026Article
- Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10-year calibration plots demonstrated that using the standard approach over-estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis.
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Registered trials
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