![]() ![]() Nonetheless, our current model appears to be able to detect differences between the two populations in the early phase of the competitive year, with about 1 meter difference in performance between doped and non-doped athletes (see Figure 4). Moreover, factors such as individual athlete seasonal training and competition patterns will affect their individual competition results, independently of any doping related effect and so must be accounted for in any longitudinal model of performance. Further, the model does not currently accommodate longitudinal covariates potentially affecting performance, for example, as shown in Figure 5, athlete aging has a clear demonstrable effect on shot put throwing distance. Thus, our dataset shows more local, short-range variability, which the current version of the model cannot adequately represent. ![]() ![]() Instead, the shot put dataset has measurements often collected just a few days apart from each other on each athlete, and for a potentially long number of years. The Bayesian latent factor regression methodology was originally developed for very sparse longitudinal data ( Montagna et al., 2012) with the purpose of capturing a global trend in subject-specific trajectories. of potential issues with the data, the model in its current formulation suffers from some limitations.
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