![]() ![]() ![]() retrospective studies and observational vs. There are several classes of longitudinal study designs, including prospective vs. Characterisation of such risk factors is essential in understanding disease pathogenesis, as well as in assessing an individuals’ disease risk, patient stratification, treatment choice evaluation, in a future personalised medicine paradigm, and planning disease prevention strategies. Longitudinal studies are effective in identifying various risk factors that are associated with an outcome, such as disease initiation, disease onset or any disease-associated molecular biomarker. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.īiomedical research often involves longitudinal studies where individuals are followed over a period of time and measurements are repeatedly collected from the subjects of the study. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. ![]()
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