Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial

DOI10.1214/12-AOAS555zbMath1257.62113arXiv1301.2490OpenAlexW2047802799WikidataQ30600783 ScholiaQ30600783MaRDI QIDQ1940020

Juned Siddique, Catherine M. Crespi, Ofer Harel

Publication date: 5 March 2013

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

Full work available at URL: https://arxiv.org/abs/1301.2490

zbMATH Keywords

nonignorablemissing not at randomnot missing at randomNMARMNAR

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (6)

A hot deck imputation procedure for multiply imputing nonignorable missing data: the proxy pattern-mixture hot deck ⋮ Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review ⋮ Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment ⋮ Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial ⋮ Difference Between Binomial Proportions Using Newcombe’s Method With Multiple Imputation for Incomplete Data ⋮ Handling attrition in longitudinal studies: the case for refreshment samples

Uses Software

• MICE

Cites Work

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• Missing data methods in longitudinal studies: a review
• Partially parametric techniques for multiple imputation
• Inferences on missing information under multiple imputation and two-stage multiple imputation
• Using an approximate Bayesian bootstrap to multiply impute nonignorable missing data
• Missing data imputation using the multivariate \(t\) distribution
• Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial
• Missing Data in Longitudinal Studies
• Inferences on the Outfluence – How do Missing Values Impact Your Analysis?
• Inference and missing data
• Formalizing Subjective Notions About the Effect of Nonrespondents in Sample Surveys
• Model-based Inference for Categorical Survey Data Subject to Non-ignorable Non-response
• Sensitivity Analysis for Incomplete Contingency Tables: The Slovenian Plebiscite Case
• Sensitivity Analysis of Continuous Incomplete Longitudinal Outcomes
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Multiple imputation of discrete and continuous data by fully conditional specification
• Sensitivity analysis after multiple imputation under missing at random: a weighting approach
• Strategies to fit pattern-mixture models
• A Bayesian Approach for Clustered Longitudinal Ordinal Outcome With Nonignorable Missing Data
• Longitudinal Data Analysis

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