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BSU Seminar: "Upgrading survival models with CARE"

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If you have a question about this talk, please contact Alison Quenault .

This will be a free hybrid seminar. To register to attend remotely, please click here: https://cam-ac-uk.zoom.us/meeting/register/M66a6T77T9-0PCylnJfB7g

Clinical risk prediction models are regularly updated as new data, often with additional covariates, become available. We propose CARE (Convex Aggregation of relative Risk Estimators) as a general approach for combining existing “external” estimators with a new data set in a time-to-event survival analysis setting. Our method initially employs the new data to fit a flexible family of reproducing kernel estimators via penalised partial likelihood maximisation. The final relative risk estimator is then constructed as a convex combination of the kernel and external estimators, with the convex combination coefficients and regularisation parameters selected using cross-validation. We establish high-probability bounds for the L2-error of our proposed aggregated estimator, showing that it achieves a rate of convergence that is at least as good as both the optimal kernel estimator and the best external model. Empirical results from simulation studies align with the theoretical results, and we illustrate the improvements our methods provide for cardiovascular disease risk modelling.

This talk is part of the MRC Biostatistics Unit Seminars series.

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