Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity-Reference-Cited by-同舟云学术

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Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity

Published:2021-06-16 Issue:23 Volume:40 Page:4915-4930
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Sheng Ying¹,Sun Yifei²^ORCID,Huang Chiung‐Yu¹³^ORCID,Kim Mi‐Ok¹³^ORCID

Affiliation:

1. Department of Epidemiology & Biostatistics University of California at San Francisco San Francisco California USA

2. Department of Biostatistics, Mailman School of Public Health Columbia University New York USA

3. Helen Diller Family Comprehensive Cancer Center University of California at San Francisco San Francisco California USA

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9101

Reference39 articles.

1. Covariate heterogeneity in meta-analysis: Criteria for deciding between meta-regression and individual patient data

2. Generalized linear models incorporating population level information: an empirical-likelihood-based approach

3. Aggregate-data estimation of an individual patient data linear random effects meta-analysis with a patient covariate-treatment interaction term

4. Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources

5. Empirical likelihood estimation using auxiliary summary information with different covariate distributions;Han P;Stat Sin,2019

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrative analysis of individual-level data and high-dimensional summary statistics;Bioinformatics;2023-03-25

2. Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity;Biometrics;2022-11-10

3. A review of recent advances in empirical likelihood;WIREs Computational Statistics;2022-09-20

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