Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features
| Autor: | Hanze Zhang, Henian Chen, Barbara Langland-Orban, Yangxin Huang, Wei Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Epidemiology Computer science 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Health Information Management Limit of Detection Bayesian multivariate linear regression Statistics Covariate Linear regression Econometrics Humans Longitudinal Studies 030212 general & internal medicine 0101 mathematics Acquired Immunodeficiency Syndrome Nonparametric statistics Bayes Theorem Viral Load CD4 Lymphocyte Count Quantile regression Outlier Linear Models Bayesian linear regression Quantile |
| Zdroj: | Statistical Methods in Medical Research. 28:569-588 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280217730852 |
| Popis: | In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios. |
| Databáze: | OpenAIRE |
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