Identification of causal effects on binary outcomes using structural mean models

DOI10.1093/biostatistics/kxq024zbMath1437.62423OpenAlexW2134963223WikidataQ42874363 ScholiaQ42874363MaRDI QIDQ3303637

Paul S. Clarke, Frank A. G. Windmeijer

Publication date: 4 August 2020

Published in: Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biostatistics/kxq024

Mathematics Subject Classification ID

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

Related Items (2)

Estimating mode effects from a sequential mixed-mode experiment using structural moment models ⋮ A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting

Cites Work

• Semiparametric instrumental variable estimation of treatment response models.
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• Identification of Causal Effects Using Instrumental Variables
• Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models
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• Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome
• Causal Inference with Generalized Structural Mean Models
• Correcting for non-compliance in randomized trials using structural nested mean models
• Estimation of Treatment Effects in Randomized Trials With Non-Compliance and a Dichotomous Outcome
• Estimating the Effect of Treatment in a Proportional Hazards Model in the Presence of Non-Compliance and Contamination
• Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure
• Mendelian randomization as an instrumental variable approach to causal inference

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