Identifying risk factors from longitudinal data requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable. Here, we present a widely applicable and interpretable probabilistic machine learning method for nonparametric longitudinal data analysis using additive Gaussian process regression . We demonstrate that it outperforms previous longitudinal modeling approaches and provides useful novel features, including the ability to account for uncertainty in disease effect times as well as heterogeneity in their effects .
Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data , however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. We present lgpr, a widely applicable and interpretable method for nonparametric analysis of longitudinal data using additive Gaussian processes . We demonstrate that it outperforms previous approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important novel features, including the ability to account for the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package. lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data, and code for reproducing the experiments of this paper .
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Identifying risk factors
Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects
R
requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable. Here, we present
, however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. We present lgpr,
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probabilistic machine learning
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longitudinal data analysis
analysis of longitudinal data
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process regression
processes
R
longitudinal modeling approaches and provides useful
approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important
R
uncertainty in disease effect times as well as heterogeneity in their effects
the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package. lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data, and code for reproducing the experiments of this paper