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A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
BackgroundMissing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches...
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Detecting potential outliers in longitudinal data with time-dependent covariates
BackgroundOutliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating...
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Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data
BackgroundIn clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific...
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A terminal trend model for longitudinal medical cost data and survival
A joint modeling approach on survival and longitudinal data has proven to be valuable in end of life applications, especially when there is a strong...
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Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system
BackgroundSeveral strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the...
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A bias-reduced generalized estimating equation approach for proportional odds models with small-sample longitudinal ordinal data
BackgroundLongitudinal ordinal data are commonly analyzed using a marginal proportional odds model for relating ordinal outcomes to covariates in the...
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Using collective intelligence methods to improve government data infrastructures and promote the use of complex data: The example of the Northern Ireland Longitudinal Study
BackgroundThis paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to...
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Detecting influential subjects in intensive longitudinal data using mixed-effects location scale models
BackgroundCollection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on...
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Amygdala volumes and associations with socio-emotional competencies in preterm youth: cross-sectional and longitudinal data
BackgroundSocio-emotional difficulties often result from very preterm (VPT) birth. The amygdala’s developmental trajectory, including its nuclei, has...
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Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic
BackgroundDuring the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public...
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Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors
BackgroundTechnology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups...
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Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline
BackgroundAlzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no...
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Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach
BackgroundIntensive longitudinal data (ILD) collected in near real time by mobile health devices provide a new opportunity for monitoring chronic...
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Psychosis Symptom Trajectories Across Childhood and Adolescence in Three Longitudinal Studies: An Integrative Data Analysis with Mixture Modeling
Psychotic-like experiences (PLEs) are common throughout childhood, and the presence of these experiences is a significant risk factor for poor mental...
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Predicting the risk of a clinical event using longitudinal data: the generalized landmark analysis
BackgroundIn the development of prediction models for a clinical event, it is common to use the static prediction modeling (SPM), a regression model...
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Predictors of male loneliness across life stages: an Australian study of longitudinal data
BackgroundDespite growing recognition of loneliness as a global public health concern, research on its occurrence and precipitants among men across...
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Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study
BackgroundMultivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across...
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A method for generating synthetic longitudinal health data
Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy...
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A joint model of longitudinal pharmacokinetic and time-to-event data to study exposure–response relationships: a proof-of-concept study with alectinib
PurposeIn exposure–response analyses of oral targeted anticancer agents, longitudinal plasma trough concentrations are often aggregated into a single...
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Simulation study comparing analytical methods for single-item longitudinal patient-reported outcomes data
PurposeEfficient analytical methods are necessary to make reproducible inferences on single-item longitudinal ordinal patient-reported outcome (PRO)...
