A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model

AbstractMotivationBiomedical 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. General linear mixed effect models have become the standard workhorse for statistical analysis of data from longitudinal study designs. However, analysis of longitudinal data can be complicated for both practical and theoretical reasons, including difficulties in modelling, correlated outcome values, functional (time-varying) covariates, nonlinear effects, and model inference.ResultsWe present LonGP, an additive Gaussian process regression model for analysis of experimental data from longitudinal study designs. LonGP implements a flexible, non-parametric modelling framework that solves commonly faced challenges in longitudinal data analysis. In addition to inheriting all standard features of Gaussian processes, 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 develop an accurate Bayesian inference and model selection method, and implement an efficient model search algorithm for our additive Gaussian process model. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets. Our work is accompanied by a versatile software implementation.AvailabilityLonGP software tool is available athttp://research.cs.aalto.fi/csb/software/longp/[email protected],[email protected]

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Beyond baseline data: the use of time-varying covariates

Journal of Hypertension ◽

10.1097/hjh.0b013e3282fcbc22 ◽

2008 ◽

Vol 26 (4) ◽

pp. 639-640 ◽

Cited By ~ 7

Author(s):

Ralph B DʼAgostino

Keyword(s):

Baseline Data ◽

Time Varying ◽

Use Of Time ◽

Time Varying Covariates

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Collinearity issues in autoregressive models with time-varying serially dependent covariates

10.31234/osf.io/96snh ◽

2021 ◽

Author(s):

Sigert Ariens ◽

Janne Adolf ◽

Eva Ceulemans

Keyword(s):

Autoregressive Model ◽

Observational Studies ◽

Temporal Dynamics ◽

Autoregressive Models ◽

Time Varying ◽

Serial Dependence ◽

Psychological Science ◽

Use Of Time ◽

Different Types ◽

Time Varying Covariates

Autoregressive models are becoming an increasingly popular tool in psychological science, and are typically used to assess the temporal dynamics of a univariate process. Their popularity has led researchers to extend the basic autoregressive model to include theoretically relevant time-varying covariates, such as experimental stimuli or contextual factors in observational studies. Including covariates in an autoregressive model can however hamper estimation of such models due to predictor collinearity. As we show in this paper, predictor collinearity in AR(1) models with time-varying covariates can emerge as a function of the serial dependence within the variables involved. We provide an analytic study of the collinearity issue and a simulation study to showcase that it generalizes across different types of time-varying covariates. Implications for study design, the use of time as a predictor, and related model variants are discussed.

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