Detection of atypical response trajectories in biomedical longitudinal databases
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Lucio José Pantazis
and
Rafael Antonio García
Abstract
Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.
Funding source: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This study was funded by Consejo Nacional de Investigaciones Cientficas y Tcnicas (CONICET).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
