A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials

DOI10.1111/j.1541-0420.2009.01303.xzbMath1192.62229OpenAlexW2084935411WikidataQ36002474 ScholiaQ36002474MaRDI QIDQ3576929

Yun Li, Michael R. Elliott

Publication date: 3 August 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3365598

zbMATH Keywords

Bayesian estimationsurrogate markerrandomized trialcounterfactual model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Medical applications (general) (92C50)

Related Items (12)

Comparing Biomarkers as Principal Surrogate Endpoints ⋮ On the relationship between the causal‐inference and meta‐analytic paradigms for the validation of surrogate endpoints ⋮ Adaptive neural network surrogate model for solving the implied volatility of time-dependent American option via Bayesian inference ⋮ Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program ⋮ A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint ⋮ Identification of causal effects in the presence of nonignorable missing outcome values ⋮ A causal framework for surrogate endpoints with semi-competing risks data ⋮ Statistical evaluation of surrogate endpoints with examples from cancer clinical trials ⋮ Discussion on “Surrogate Measures and Consistent Surrogates” ⋮ An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference ⋮ A Multiple Imputation Approach for the Evaluation of Surrogate Markers in the Principal Stratification Causal Inference Framework ⋮ Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials

Cites Work

• Bayesian inference for causal effects in randomized experiments with noncompliance
• On model expansion, model contraction, identifiability and prior information: two illustrative scenarios involving mismeasured variables
• Simple relations between principal stratification and direct and indirect effects
• Bayesian inference for causal effects: The role of randomization
• Prior induction in log-linear models for general contingency table analysis.
• Principal Stratification in Causal Inference
• A Measure of the Proportion of Treatment Effect Explained by a Surrogate Marker
• A Bayesian Hierarchical Model for Categorical Data with Nonignorable Nonresponse
• Evaluating Candidate Principal Surrogate Endpoints
• Related Causal Frameworks for Surrogate Outcomes
• Bounds on Treatment Effects From Studies With Imperfect Compliance
• The validation of surrogate endpoints in meta-analyses of randomized experiments
• Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models
• Criteria for the Validation of Surrogate Endpoints in Randomized Experiments
• Properties of A Nonparametric Test for Early Comparison of Treatments in Clinical Trials in the Presence of Surrogate Endpoints
• Latent Class Model Diagnosis
• Surrogate Marker Evaluation from an Information Theory Perspective
• Parameterization and Bayesian Modeling

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