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

Author:

Booth Sarah1ORCID,Mozumder Sarwar I.12ORCID,Archer Lucinda3ORCID,Ensor Joie3ORCID,Riley Richard D.3ORCID,Lambert Paul C.14ORCID,Rutherford Mark J.1ORCID

Affiliation:

1. Biostatistics Research Group, Department of Population Health Sciences University of Leicester Leicester UK

2. Oncology Biometrics Statistical Innovation, AstraZeneca Cambridge UK

3. Institute of Applied Health Research, College of Medical and Dental Sciences University of Birmingham Birmingham UK

4. Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden

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.

Funder

Cancer Research UK

Cancerfonden

National Institute for Health and Care Research

UK Research and Innovation

VR

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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