On the performance of random‐coefficient pattern‐mixture models for non‐ignorable drop‐out
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Summary
It is shown that alternative RCPMMs that fit equally well may lead to very different estimates for parameters of interest, and that minor model misspecification can introduce biases that are quite large relative to standard errors, even in fairly small samples.
- Type
- article
- Published
- 2003-08-30
- Cited by
- 167
- References
- 32
- Access
- Open access
- OpenAlex
- https://openalex.org/W1998331851
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:22571539
Keywords
Extrapolation, Statistics, Imputation (statistics), Econometrics, Random effects model
References
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- Multiple Imputation for Missing Data: Concepts and New Development
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- Multiple-Imputation Inferences with Uncongenial Sources of Input
- Mixed-Effects Models in S and S-PLUS
- A comparison of inclusive and restrictive strategies in modern missing data procedures.
- Mixture models for the joint distribution of repeated measures and event times.
- INFERENCE AND MISSING DATA
- Monotone missing data and pattern‐mixture models
- Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values
- Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies
- Estimation and comparison of changes in the presence of informative right censoring: conditional linear model.
- Some Statistical Properties of a Family of Continuous Univariate Distributions
- Pattern-mixture models for multivariate incomplete data with covariates.
- Selection models for repeated measurements with non-random dropout: an illustration of sensitivity.
- Modeling the Drop-Out Mechanism in Repeated-Measures Studies
- Intent-to-treat analysis for longitudinal studies with drop-outs.
- A multiple imputation strategy for incomplete longitudinal data
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- Random-effects models for longitudinal data.
Cited by
- Regression modeling with missing outcomes : competing risks and longitudinal data
- Pattern-Mixture Models for Addressing Nonignorable Nonresponse in Longitudinal Substance Abuse Treatment Studies
- The Treatment of Missing Data when Estimating Student Growth with Pre-Post Educational Accountability Data
- Modeling Incomplete Longitudinal Data
- Fehlende Werte in sportwissenschaftlichen Untersuchungen
- Variation in cancer outcomes amongst children and young adults in Yorkshire
- Applied Longitudinal Data Analysis for Epidemiology
- Multiple Imputation by Chained Equations: An Overview of Conceptual and Operational Aspects and Software: Review
- Different Types of Missing in Longitudinal Data and the Likelihood-base Methods Applied in their Analysis
- Joint modeling of missing data due to non‐participation and death in longitudinal aging studies by K. B. Rajan and S. E. Leurgans, Statistics in Medicine 2010; 29:2260–2268
- MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors
- Analyzing longitudinal data with missing values.
- Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis.
- Attrition in randomized controlled clinical trials: methodological issues in psychopharmacology.
- Bias in longitudinal data analysis with missing data using typical linear mixed-effects modelling and pattern-mixture approach: an analytical illustration.
- The impact of missing data on estimation of health-related quality of life outcomes: an analysis of a randomized longitudinal clinical trial
- On imputing continuous data when the eventual interest pertains to ordinalized outcomes via threshold concept
- Gaussianization‐based quasi‐imputation and expansion strategies for incomplete correlated binary responses
- Treatment of Missing Data in Workforce Education Research.
- A latent class selection model for nonignorably missing data
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