Abstract:
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The SuperImposition by Translation And Rotation (SITAR) model, a shape invariant model, has recently become popular as it factors the growth pattern into the vertical shift, horizontal shift, and horizontal stretch. This model can also be applied to longitudinal data such as 24-hour ambulatory blood pressure by using the periodic spline function. The key challenge of estimating the parameters of the SITAR model is the high dependency among the parameters. Two methods among others, the R package ‘sitar,’ a frequentist approach, and the ‘rjags’ package, a Bayesian approach, can be used for estimation. Recently, the Hamiltonian Monte Carlo (HMC) method within the Bayesian framework performed better in estimating the highly correlated parameters. However, this has not yet been explored with the growth curve model. Using the 24-hour blood pressure data measured in every 15/30 minutes for hypertension subjects, we compared the HMC estimates with that obtained from ‘sitar’ and ‘rjags’. We observed that HMC provides better parameter estimates and model diagnosis. Our future works will include implementing the periodic spline function within the HMC algorithm.
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