Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data
Abstract Background In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a gre...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02164-y |
| _version_ | 1827327078369853440 |
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| author | Melkamu M. Ferede Getachew A. Dagne Samuel M. Mwalili Workagegnehu H. Bilchut Habtamu A. Engida Simon M. Karanja |
| author_facet | Melkamu M. Ferede Getachew A. Dagne Samuel M. Mwalili Workagegnehu H. Bilchut Habtamu A. Engida Simon M. Karanja |
| author_sort | Melkamu M. Ferede |
| collection | DOAJ |
| description | Abstract Background In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. Methods This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. Results To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. Conclusions The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution. |
| first_indexed | 2024-03-07T14:55:49Z |
| format | Article |
| id | doaj.art-c52e14e917aa4495a0cdc6e712f3c777 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-07T14:55:49Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-c52e14e917aa4495a0cdc6e712f3c7772024-03-05T19:28:42ZengBMCBMC Medical Research Methodology1471-22882024-03-0124111110.1186/s12874-024-02164-yFlexible Bayesian semiparametric mixed-effects model for skewed longitudinal dataMelkamu M. Ferede0Getachew A. Dagne1Samuel M. Mwalili2Workagegnehu H. Bilchut3Habtamu A. Engida4Simon M. Karanja5Department of Statistics, University of GondarDepartment of Epidemiology and Biostatistics, College of Public Health, University of South FloridaDepartment of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT)Department of Internal Medicine, College of Medicine and Health Sciences, University of GondarDepartment of Mathematics, Debre Markos UniversitySchool of Public Health, Jomo Kenyatta University of Agriculture and Technology (JKUAT)Abstract Background In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. Methods This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. Results To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. Conclusions The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.https://doi.org/10.1186/s12874-024-02164-yBayesian inferenceSemiparametric mixed-modelsLongitudinal dataSkew-distributionsChronic kidney disease |
| spellingShingle | Melkamu M. Ferede Getachew A. Dagne Samuel M. Mwalili Workagegnehu H. Bilchut Habtamu A. Engida Simon M. Karanja Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data BMC Medical Research Methodology Bayesian inference Semiparametric mixed-models Longitudinal data Skew-distributions Chronic kidney disease |
| title | Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data |
| title_full | Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data |
| title_fullStr | Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data |
| title_full_unstemmed | Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data |
| title_short | Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data |
| title_sort | flexible bayesian semiparametric mixed effects model for skewed longitudinal data |
| topic | Bayesian inference Semiparametric mixed-models Longitudinal data Skew-distributions Chronic kidney disease |
| url | https://doi.org/10.1186/s12874-024-02164-y |
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