Estimating functions for evaluating treatment effects in cluster‐randomized longitudinal studies in the presence of drop‐out and non‐compliance

DOI10.1002/cjs.10058zbMath1349.62214OpenAlexW2129637723MaRDI QIDQ4932235

Baojiang Chen, Grace Y. Yi, Richard J. Cook

Publication date: 1 October 2010

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

Full work available at URL: https://doi.org/10.1002/cjs.10058

zbMATH Keywords

longitudinal dataestimating functioncompliancetreatment efficacy

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Generalized linear models (logistic models) (62J12)

Related Items (1)

Regression analysis of correlated ordinal data using orthogonalized residuals

Cites Work

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• Marginal and association regression models for longitudinal binary data with drop-outs: A likelihood-based approach
• Modelling multivariate binary data with alternating logistic regressions
• Marginal Methods for Incomplete Longitudinal Data Arising in Clusters
• Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• A mean score method for missing and auxiliary covariate data in regression models
• Informative Drop-Out in Longitudinal Data Analysis

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