Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes

DOI10.1111/biom.12519zbMath1390.62300arXiv1507.01822OpenAlexW1802854002WikidataQ37322419 ScholiaQ37322419MaRDI QIDQ5355226

Eric J. Tchetgen Tchetgen, Rui Wang, Alisa J. Stephens, Mélanie Prague, Victor de Gruttola

Publication date: 7 September 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.01822

zbMATH Keywords

interactionsaugmentationinterferencepropensity scoreinverse probability weighting (IPW)missing at random (MAR)semi-parametric methodsgeneralized estimating equation (GEE)cluster-randomized trialsoutcome model

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

A Stochastic Second-Order Generalized Estimating Equations Approach for Estimating Association Parameters ⋮ An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness

Uses Software

• R
• PROC GENMOD
• geeM

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