Multivariate survival analysis in big data: A divide‐and‐combine approach

DOI10.1111/biom.13469zbMath1520.62364OpenAlexW3156685327MaRDI QIDQ6079569

Wei Wang, Jerry Q. Cheng, Unnamed Author, Min-ge Xie, Shou-En Lu

Publication date: 30 October 2023

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.13469

zbMATH Keywords

regularizationproportional hazards modelvariable selectionbig datamarginal modelconfidence distributionmultivariate failure timedivide and combine

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Analysis of multivariate survival data
• A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials
• Distributed simultaneous inference in generalized linear models via confidence distribution
• On the ``degrees of freedom of the lasso
• Fixed and Random Effects Selection in Mixed Effects Models
• The Highest Confidence Density Region and Its Usage for Joint Inferences about Constrained Parameters
• A Solution to the Problem of Monotone Likelihood in Cox Regression
• Variable selection for high dimensional multivariate outcomes
• A split-and-conquer approach for analysis of
• On the relative efficiency of using summary statistics versus individual-level data in meta-analysis
• Unified LASSO Estimation by Least Squares Approximation
• Multivariate Generalizations of the Proportional Hazards Model
• Bivariate Survival Models Induced by Frailties
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Note on the Efficiency of Sandwich Covariance Matrix Estimation
• Marginal Regression Models for Multivariate Failure Time Data
• On the optimality of averaging in distributed statistical learning
• Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review
• Discussion
• Communication-Efficient Distributed Statistical Inference
• Case‐Cohort Designs and Analysis for Clustered Failure Time Data
• Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Adaptive Lasso for Cox's proportional hazards model
• Variable selection for multivariate failure time data

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