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Statistics in Medicine - 2023 - Booth - Using temporal recalibration to improve the calibration of risk prediction models.pdf (2.98 MB)

Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time

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posted on 2023-11-08, 15:11 authored by S Booth, SI Mozumder, L Archer, J Ensor, RD Riley, PC Lambert, MJ Rutherford

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.


Cancer Research UK. Grant Numbers: C14183/A29739, C41379/A27583

Cancerfonden. Grant Number: 2018/744

National Institute for Health and Care Research. Grant Number: NIHR300100

UK Research and Innovation

Vetenskapsrådet. Grant Number: 2017-01591


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Department of Population Health Sciences, University of Leicester


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