Estimating Causal Treatment Effects from Longitudinal HIV Natural History Studies Using Marginal Structural Models

DOI10.1111/1541-0420.00018zbMath1210.62174OpenAlexW2137883402WikidataQ47894497 ScholiaQ47894497MaRDI QIDQ3079092

Hyejin Ko, Joseph W. Hogan, Kenneth H. Mayer

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00018

zbMATH Keywords

counterfactualsnoncompliancesensitivity analysisrepeated measuressemiparametric modelselection biasHAARTobservational studyinverse-probability weightingunmeasured confoundingdropouttime-dependent confoundingCD4 countendogenous treatmentacquired immune deficiency syndrome (AIDS)

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02)

Related Items

Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study ⋮ Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models ⋮ Sensitivity Analysis of Unmeasured Confounding for the Causal Risk Ratio by Applying Marginal Structural Models ⋮ Sensitivity analysis for causal inference using inverse probability weighting

Cites Work

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• Association, causation, and marginal structural models.
• Identification of Causal Effects Using Instrumental Variables
• Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
• Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome
• Causal Inference Without Counterfactuals
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data

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