Dynamic survival prediction combining landmarking with a machine learning ensemble: methodology and empirical comparison

Tanner, Kamaryn T., Sharples, Linda D., Daniel, Rhian M. ORCID: https://orcid.org/0000-0001-5649-9320 and Keogh, Ruth H. 2021. Dynamic survival prediction combining landmarking with a machine learning ensemble: methodology and empirical comparison. Journal of the Royal Statistical Society: Series A 184 (1) , pp. 3-30. 10.1111/rssa.12611

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Abstract

Dynamic prediction models provide predicted survival probabilities that can be updated over time for an individual as new measurements become available. Two techniques for dynamic survival prediction with longitudinal data dominate the statistical literature: joint modelling and landmarking. There is substantial interest in the use of machine learning methods for prediction; however, their use in the context of dynamic survival prediction has been limited. We show how landmarking can be combined with a machine learning ensemble—the Super Learner. The ensemble combines predictions from different machine learning and statistical algorithms with the goal of achieving improved performance. The proposed approach exploits discrete time survival analysis techniques to enable the use of machine learning algorithms for binary outcomes. We discuss practical and statistical considerations involved in implementing the ensemble. The methods are illustrated and compared using longitudinal data from the UK Cystic Fibrosis Registry. Standard landmarking and the landmark Super Learner approach resulted in similar cross‐validated predictive performance, in this case, outperforming joint modelling.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Additional Information:	This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher:	Royal Statistical Society
ISSN:	0964-1998
Date of First Compliant Deposit:	18 March 2021
Date of Acceptance:	5 June 2020
Last Modified:	02 May 2023 13:10
URI:	https://orca.cardiff.ac.uk/id/eprint/139869

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