Mixed hidden Markov quantile regression models for longitudinal data with possibly incomplete sequences.
Stat Methods Med Res
; 27(7): 2231-2246, 2018 07.
Article
in En
| MEDLINE
| ID: mdl-27899706
ABSTRACT
Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters. However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model, where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large-scale simulation study and the analysis of the CD4 count data.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Regression Analysis
/
Markov Chains
/
Longitudinal Studies
Type of study:
Diagnostic_studies
/
Health_economic_evaluation
/
Observational_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Stat Methods Med Res
Year:
2018
Document type:
Article
Affiliation country:
Italy