A latent autoregressive model for longitudinal binary data subject to informative missingness

Biometrics

Volumn 58, Issue 3, 2002, Pages 631-642

  Albert, Paul S ^a   Follmann, Dean A ^b   Wang, Shaohua A ^c   Suh, Edward B ^c  

^a NATIONAL CANCER INSTITUTE   (United States)

^b NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Informative missingness; Longitudinal data; Nonignorable missing data; Repeated binary data

Indexed keywords

BODY FLUIDS; MEDICAL APPLICATIONS; MONTE CARLO METHODS; RANDOM PROCESSES;

AUTOREGRESSIVE MODELLING; BINARY DATA; CLINICAL TRIAL; DATA SUBJECTS; INFORMATIVE MISSINGNESS; LONGITUDINAL BINARY DATA; LONGITUDINAL DATA; MISSING DATA; NONIGNORABLE MISSING DATA; REPEATED BINARY DATA;

AUTOCORRELATION;

METHODOLOGY;

ARTICLE; BIOMETRY; CLINICAL RESEARCH; DATA ANALYSIS; LONGITUDINAL STUDY; MONTE CARLO METHOD; STATISTICAL MODEL;

EID: 0036712328     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2002.00631.x     Document Type: Article

References (20)

1. A transitional model for longitudinal binary data subject to nonignorable missing data
2. Modeling repeated count data subject to informative dropout
3. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm
4. Monte Carlo approximation of bootstrap variances
5. An approximate generalized linear model with random effects for informative missing data
6. Joint modelling of longitudinal measurements and event time data
7. A controlled trial of buprenorphine treatment for opioid dependence
8. Maximum-likelihood algorithms for generalized linear mixed models
9. Model for the analysis of binary longitudinal pain data subject to informative dropout through remedication
10. Mixed effects logistic regression models for longitudinal binary response data with informative dropout
11. A Monte-Carlo implementation of the E-M algorithm and the poor man's data augmentation algorithms
12. Estimation and comparison of changes in the presence of informative right censoring: Conditional linear model
13. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process
14. Use of summary measures to adjust for informative missingness in repeated measures data with random effects