Influence analysis to assess sensitivity of the dropout process.

DOI10.1016/S0167-9473(00)00065-7zbMath1077.62540OpenAlexW2004068754WikidataQ127663395 ScholiaQ127663395MaRDI QIDQ5941428

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Publication date: 20 August 2001

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(00)00065-7

zbMATH Keywords

Case deletionDropoutGlobal influenceLocal influenceSelection modelSensitivity analysis

Mathematics Subject Classification ID

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

Related Items (12)

An ECM Estimation Approach for Analyzing Multivariate Skew-Normal Data with Dropout ⋮ Comments on: Missing data methods in longitudinal studies: a review. ⋮ Formal and informal model selection with incomplete data ⋮ Local influence diagnostics for incomplete overdispersed longitudinal counts ⋮ A reanalysis of a longitudinal scleroderma clinical trial using non-ignorable missingness models ⋮ Robust estimation of dropout models using hierarchical likelihood ⋮ Kernel weighted influence measures ⋮ The nature of sensitivity in monotone missing not at random models ⋮ Sensitivity analysis of longitudinal data with intermittent missing values ⋮ A Local Influence Approach Applied to Binary Data from a Psychiatric Study ⋮ Longitudinal conditional models with intermittent missingness: SAS code and applications ⋮ Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs

Cites Work

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• Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach
• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• Inference and missing data
• A class of pattern-mixture models for normal incomplete data
• The analysis of longitudinal ordinal data with nonrandom drop-out
• Local Influence in Linear Mixed Models
• Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm
• Informative Drop-Out in Longitudinal Data Analysis
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies

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