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Estimation of knots using such "mixture regression with change point" models is known to be computationally demanding 25 , and even more so with distinct outcomes, replicate observations, and numerous phases that span latent classes. Across site users, we use a parallelized local grid search strategy using commercial applications as an engine because no general purpose method scales to data of the sophistication used here and span both stages.
Our method is generalizable and replicable, and it leverages two specific software packages to split the statistical model into two parts: generating random "nearby" candidate knot configurations carried out in Matlab and analyzing discrete heterogeneity in resulting parameters completed in Latent Gold. We then use a combination of stochastic- and gradient-based methods to iterate between exploring the space of knots that are possible and estimating the class models for any given set of knots.
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