Monotone missing data and pattern‐mixture models
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Summary
It is shown that the classical taxonomy of missing data models, namely missing completely at random, missing at random and informative missingness, which has been developed almost exclusively within a selection modelling framework, can be applied to pattern‐mixture models.
- Type
- article
- Published
- 1998-06-01
- Cited by
- 134
- References
- 8
- OpenAlex
- https://openalex.org/W2014283811
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121435333
Keywords
Missing data, Mixture model, Monotone polygon, Computer science, Model selection
References
- Informative dropout in longitudinal data analysis.
- Pattern-Mixture Models for Multivariate Incomplete Data
- INFERENCE AND MISSING DATA
- Statistical Analysis With Missing Data
- Modeling the Drop-Out Mechanism in Repeated-Measures Studies
- The analysis of longitudinal ordinal data with nonrandom drop-out
- A Class of Pattern-Mixture Models for Normal Incomplete Data
- Selection Modeling Versus Mixture Modeling with Nonignorable Nonresponse
Cited by
- A graphical sensitivity analysis for clinical trials with non‐ignorable missing binary outcome
- Regression modeling with missing outcomes : competing risks and longitudinal data
- Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies
- A Comparison for Longitudinal Data Missing Due to Truncation
- Bias in mixtures of normal distributions and joint modeling of longitudinal and time-to-event data with monotonic change curves
- Análise de dados categorizados com omissão
- Applied Longitudinal Data Analysis for Epidemiology
- Modeling longitudinal data with non-random drop-outs using copulas
- Quantile regression in the presence of monotone missingness with sensitivity analysis.
- A characterization of missingness at random in a generalized shared‐parameter joint modeling framework for longitudinal and time‐to‐event data, and sensitivity analysis
- Longitudinal data with dropouts: a comparison of pattern mixture models with complete case analysis
- A Sensitivity Analysis for Shared‐Parameter Models for Incomplete Longitudinal Outcomes
- A Simple Imputation Method for Longitudinal Studies with Non‐ignorable Non‐responses
- Analyzing longitudinal data with missing values.
- The impact of missing data on the results of a schizophrenia study
- Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation
- A pattern‐mixture model for the analysis of censored quality‐of‐life data
- Pattern-Mixture Model of the Cox Proportional Hazards Model with Missing Binary Covariates
- A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies with Nonignorable Missingness with Application to an Acute Schizophrenia Clinical Trial
- Generalized shared-parameter models and missingness at random
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