Causal inference for recurrent events data with all-or-none compliance

DOI10.1080/03610926.2014.980515zbMath1349.62459OpenAlexW2520503391MaRDI QIDQ2834730

Ming Zheng, Xiang Gao

Publication date: 23 November 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2014.980515

zbMATH Keywords

EM algorithmmaximum likelihoodcounting processcausal inferenceall-or-none compliance

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Cites Work

• Correcting for non-compliance in randomized trials using rank preserving structural failure time models
• Cox's regression model for counting processes: A large sample study
• Bayesian inference for causal effects: The role of randomization
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Semiparametric Estimation and Inference for Distributional and General Treatment Effects
• A Causal Proportional Hazards Estimator for the Effect of Treatment Actually Received in a Randomized Trial with All-or-Nothing Compliance
• Semiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal Events
• Efficient estimation of semiparametric transformation models for counting processes
• Efficient nonparametric estimation of causal effects in randomized trials with noncompliance
• Identification and Estimation of Local Average Treatment Effects
• Estimating Outcome Distributions for Compliers in Instrumental Variables Models
• Semiparametric Transformation Models for Point Processes

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