Booth, S, Riley, RD ORCID:, Ensor, J ORCID:, Lambert, PC and Rutherford, MJ (2020) Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time. International Journal of Epidemiology, 49 (4). pp. 1316-1325.

Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time.pdf - Published Version
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BACKGROUND: Prognostic models are typically developed in studies covering long time periods. However, if more recent years have seen improvements in survival, then using the full dataset may lead to out-of-date survival predictions. Period analysis addresses this by developing the model in a subset of the data from a recent time window, but results in a reduction of sample size. METHODS: We propose a new approach, called temporal recalibration, to combine the advantages of period analysis and full cohort analysis. This approach develops a model in the entire dataset and then recalibrates the baseline survival using a period analysis sample.The approaches are demonstrated utilizing a prognostic model in colon cancer built using both Cox proportional hazards and flexible parametric survival models with data from 1996-2005 from the Surveillance, Epidemiology, and End Results (SEER) Program database. Comparison of model predictions with observed survival estimates were made for new patients subsequently diagnosed in 2006 and followed-up until 2015. RESULTS: Period analysis and temporal recalibration provided more up-to-date survival predictions that more closely matched observed survival in subsequent data than the standard full cohort models. In addition, temporal recalibration provided more precise estimates of predictor effects. CONCLUSION: Prognostic models are typically developed using a full cohort analysis that can result in out-of-date long-term survival estimates when survival has improved in recent years. Temporal recalibration is a simple method to address this, which can be used when developing and updating prognostic models to ensure survival predictions are more closely calibrated with the observed survival of individuals diagnosed subsequently.

Item Type: Article
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Prognostic models, temporal recalibration, period analysis, up-to-date survival predictions, flexible parametric survival models, Cox proportional hazards models
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > Q Science (General)
R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine
Divisions: Faculty of Medicine and Health Sciences > School of Primary, Community and Social Care
Related URLs:
Depositing User: Symplectic
Date Deposited: 27 Apr 2020 10:58
Last Modified: 22 Dec 2020 13:11

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