A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies With Auxiliary Covariates
From MaRDI portal
Publication:3391439
DOI10.1080/10618600.2019.1617159OpenAlexW2945962306WikidataQ100434641 ScholiaQ100434641MaRDI QIDQ3391439
Tianjian Zhou, Peter Mueller, Michael J. Daniels
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://europepmc.org/articles/pmc7531618
sensitivity analysislongitudinal dataGaussian processsemiparametric modelmissing dataBayesian inference
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- BART: Bayesian additive regression trees
- Bayesian analysis of conditional autoregressive models
- Variable selection for nonparametric Gaussian process priors: Models and computational strategies
- Bayesian approaches for missing not at random outcome data: the role of identifying restrictions
- Semiparametric theory and missing data.
- Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Missing Data in Longitudinal Studies
- Partial and latent ignorability in missing-data problems
- Bayesian curve fitting using multivariate normal mixtures
- Inference and missing data
- Sample Selection Bias as a Specification Error
- A Predictive Approach to Model Selection
- Model for the Analysis of Binary Longitudinal Pain Data Subject to Informative Dropout through Remedication
- Monotone missing data and pattern‐mixture models
- A class of pattern-mixture models for normal incomplete data
- The analysis of longitudinal ordinal data with nonrandom drop-out
- Pattern-mixture models with proper time dependence
- Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout
- Pattern–Mixture and Selection Models for Analysing Longitudinal Data with Monotone Missing Patterns
- Pattern-Mixture Models for Multivariate Incomplete Data
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
- Informative Drop-Out in Longitudinal Data Analysis
- An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
- Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection
- Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates
- A Bayesian Shrinkage Model for Incomplete Longitudinal Binary Data With Application to the Breast Cancer Prevention Trial
- A framework for Bayesian nonparametric inference for causal effects of mediation
- A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial
- Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness
- Efficient Gaussian process regression for large datasets
- Joint modelling of longitudinal measurements and event time data