An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework

DOI10.1016/j.csda.2022.107494OpenAlexW4226184340WikidataQ130418995 ScholiaQ130418995MaRDI QIDQ2143008

Alvaro Jóse Flórez, Ariel Alonso Abad, Wim Van Der Elst, Geert Molenberghs

Publication date: 30 May 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2022.107494

zbMATH Keywords

sensitivity analysismatrix completion problemcausal inferencepartial correlationrandom correlation matrixmultiple surrogate evaluation

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Bayesian ridge regression for survival data based on a vine copula-based prior

Uses Software

• Surrogate

Cites Work

• Generating random correlation matrices based on vines and extended onion method
• Generating random correlation matrices based on partial correlations
• Survival analysis with correlated endpoints. Joint frailty-copula models
• The evaluation of surrogate endpoints.
• Generating random correlation matrices with fixed values: an application to the evaluation of multivariate surrogate endpoints
• Joint density of correlations in the correlation matrix with chordal sparsity patterns
• Statistical evaluation of surrogate endpoints with examples from cancer clinical trials
• A Unified Approach to Multi-item Reliability
• An informational measure of correlation
• On the relationship between the causal‐inference and meta‐analytic paradigms for the validation of surrogate endpoints
• The central role of the propensity score in observational studies for causal effects
• Statistics and Causal Inference
• Relative Entropy Measures of Multivariate Dependence
• The validation of surrogate endpoints in meta-analyses of randomized experiments