On Confounding, Prediction and Efficiency in the Analysis of Longitudinal and Cross‐sectional Clustered Data

DOI10.1111/J.1467-9469.2006.00555.XzbMath1150.62015OpenAlexW2102426706WikidataQ58819151 ScholiaQ58819151MaRDI QIDQ3505345

Stijn Vansteelandt

Publication date: 18 June 2008

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9469.2006.00555.x

zbMATH Keywords

semiparametric efficiency boundHAART efficient scoremarginal conditional mean model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Medical applications (general) (92C50)

Related Items (7)

The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments ⋮ Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study ⋮ Marginal structural models for partial exposure regimes ⋮ Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding ⋮ Methods for observed‐cluster inference when cluster size is informative: A review and clarifications ⋮ Comparison of causal effect estimators under exposure misclassification ⋮ Causal diagrams for interference

Cites Work

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• Identification of Causal Effects Using Instrumental Variables
• Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency
• Semiparametric efficiency bounds
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect

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