A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint

DOI10.1111/J.1541-0420.2011.01736.XzbMath1272.62106OpenAlexW2118135341WikidataQ41812285 ScholiaQ41812285MaRDI QIDQ4649074

Corwin M. Zigler, Thomas R. Belin

Publication date: 19 November 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3860118

zbMATH Keywords

principal stratificationsurrogate endpointbiomarkercausal effect predictiveness

Mathematics Subject Classification ID

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

Related Items (2)

Causal inference: a missing data perspective ⋮ Identification of causal effects within principal strata using auxiliary variables

Cites Work

• Interval estimation for messy observational data
• Bayesian inference for causal effects: The role of randomization
• Inference from iterative simulation using multiple sequences
• Assessing surrogate endpoints in vaccine trials with case-cohort sampling and the Cox model
• Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials
• Principal Stratification in Causal Inference
• Sampling-Based Approaches to Calculating Marginal Densities
• Evaluating Candidate Principal Surrogate Endpoints
• A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials
• Principal Stratification for Causal Inference With Extended Partial Compliance
• Augmented Designs to Assess Immune Response in Vaccine Trials

This page was built for publication: A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint