nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models
Abstract Background The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows the interpretation of the defined parameters to understand longitudinal trajector...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-023-02075-4 |
| _version_ | 1797376925715398656 |
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| author | Ana W. Capuano Maude Wagner |
| author_facet | Ana W. Capuano Maude Wagner |
| author_sort | Ana W. Capuano |
| collection | DOAJ |
| description | Abstract Background The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporating recent developments to fit the Piecewise Mixed Model with random change. Results To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of the piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive . Conclusions The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users. |
| first_indexed | 2024-03-08T19:45:37Z |
| format | Article |
| id | doaj.art-9f0f1269a4964d13a002b0dd9505188c |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-08T19:45:37Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-9f0f1269a4964d13a002b0dd9505188c2023-12-24T12:22:23ZengBMCBMC Medical Research Methodology1471-22882023-11-0123111510.1186/s12874-023-02075-4nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed modelsAna W. Capuano0Maude Wagner1RUSH Alzheimer’s Disease Center, Rush University Medical Center, 1750 HarrisonRUSH Alzheimer’s Disease Center, Rush University Medical Center, 1750 HarrisonAbstract Background The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporating recent developments to fit the Piecewise Mixed Model with random change. Results To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of the piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive . Conclusions The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users.https://doi.org/10.1186/s12874-023-02075-4Longitudinal outcomenliveNon-linear mixed modelRandom changepoint modelR packageSaemix |
| spellingShingle | Ana W. Capuano Maude Wagner nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models BMC Medical Research Methodology Longitudinal outcome nlive Non-linear mixed model Random changepoint model R package Saemix |
| title | nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models |
| title_full | nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models |
| title_fullStr | nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models |
| title_full_unstemmed | nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models |
| title_short | nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models |
| title_sort | nlive an r package to facilitate the application of the sigmoidal and random changepoint mixed models |
| topic | Longitudinal outcome nlive Non-linear mixed model Random changepoint model R package Saemix |
| url | https://doi.org/10.1186/s12874-023-02075-4 |
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