Bayesian Cox regression for large-scale inference with applications to electronic health records-Reference-Cited by-同舟云学术

Bayesian Cox regression for large-scale inference with applications to electronic health records

Published:2023-06-01 Issue:2 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Jung Alexander Wolfgang¹,Gerstung Moritz²

Affiliation:

1. European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI

2. Division of AI in Oncology, German Cancer Research Centre DKFZ

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference51 articles.

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2. Andersen, P. K., Borgan, Ø., Gill, R. D. and Keiding, N. (1993). Statistical Models Based on Counting Processes. Springer Series in Statistics. Springer, New York.

3. Sinha, D. (1993). Semiparametric Bayesian analysis of multiple event time data. J. Amer. Statist. Assoc. 88 979–983.

4. MITTAL, S., MADIGAN, D., BURD, R. S. and SUCHARD, M. A. (2014). High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics 15 207–221.

5. SUDLOW, C., GALLACHER, J., ALLEN, N., BERAL, V., BURTON, P., DANESH, J., DOWNEY, P., ELLIOTT, P., GREEN, J. et al. (2015). UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12 e1001779.

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