Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset

Di Wang, Wen Ye, Kevin He
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:232-239, 2021.

Abstract

Existing literature for prediction of time-to-event data has primarily focused on risk factors from a single individual-level dataset. However, these analyses may suffer from small sample sizes, high dimensionality and low signal-to-noise ratios. To improve prediction stability and better understand risk factors associated with outcomes of interest, we propose a Kullback-Leibler-based discrete relative risk modeling procedure to borrow information from existing models. Simulations and real data analysis were conducted to show the advantage of the proposed method compared with those solely based on data from current study or prior information.

Cite this Paper

BibTeX
@InProceedings{pmlr-v146-wang21b, title = {Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset}, author = {Wang, Di and Ye, Wen and He, Kevin}, booktitle = {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021}, pages = {232--239}, year = {2021}, editor = {Greiner, Russell and Kumar, Neeraj and Gerds, Thomas Alexander and van der Schaar, Mihaela}, volume = {146}, series = {Proceedings of Machine Learning Research}, month = {22--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v146/wang21b/wang21b.pdf}, url = {https://proceedings.mlr.press/v146/wang21b.html}, abstract = {Existing literature for prediction of time-to-event data has primarily focused on risk factors from a single individual-level dataset. However, these analyses may suffer from small sample sizes, high dimensionality and low signal-to-noise ratios. To improve prediction stability and better understand risk factors associated with outcomes of interest, we propose a Kullback-Leibler-based discrete relative risk modeling procedure to borrow information from existing models. Simulations and real data analysis were conducted to show the advantage of the proposed method compared with those solely based on data from current study or prior information.} }
Endnote
%0 Conference Paper %T Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset %A Di Wang %A Wen Ye %A Kevin He %B Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021 %C Proceedings of Machine Learning Research %D 2021 %E Russell Greiner %E Neeraj Kumar %E Thomas Alexander Gerds %E Mihaela van der Schaar %F pmlr-v146-wang21b %I PMLR %P 232--239 %U https://proceedings.mlr.press/v146/wang21b.html %V 146 %X Existing literature for prediction of time-to-event data has primarily focused on risk factors from a single individual-level dataset. However, these analyses may suffer from small sample sizes, high dimensionality and low signal-to-noise ratios. To improve prediction stability and better understand risk factors associated with outcomes of interest, we propose a Kullback-Leibler-based discrete relative risk modeling procedure to borrow information from existing models. Simulations and real data analysis were conducted to show the advantage of the proposed method compared with those solely based on data from current study or prior information.
APA
Wang, D., Ye, W. & He, K.. (2021). Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, in Proceedings of Machine Learning Research 146:232-239 Available from https://proceedings.mlr.press/v146/wang21b.html.

Related Material