An efficient doubly-robust imputation framework for longitudinal dropout, with an application to an Alzheimer's clinical trial

DOI10.1214/23-AOAS1728arXiv2305.02849MaRDI QIDQ6138572

Karen Messer, Yuqi Qiu

Publication date: 16 January 2024

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2305.02849

zbMATH Keywords

Alzheimer's diseasedoubly-robust estimatorimputation methodsrandomized trialsmonotone dropouts

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Longitudinal data analysis using generalized linear models
• Introduction to double robust methods for incomplete data
• Semiparametric theory and missing data.
• Doubly robust multiple imputation using kernel-based techniques
• Improved double-robust estimation in missing data and causal inference models
• Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout
• Doubly robust nonparametric multiple imputation for ignorable missing data
• The Generalized Estimating Equation Approach When Data Are Not Missing Completely at Random
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness
• Strictly Proper Scoring Rules, Prediction, and Estimation

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