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Background: This study aimed to compare developmental courses of structural and pragmatic language skills in school-aged children with Williams syndrome (WS) and children with idiopathic intellectual disability (IID). Comparison of these language trajectories could highlight syndrome-specific developmental features. Method: Twelve monolingual Dutch-speaking children with WS aged 5.10 to 13.3 years were assessed by means of standardised structural language tests measuring receptive and expressive vocabulary and sentence comprehension and production. Pragmatic language was evaluated by means of an expressive referential communication task and a retelling test. All of these language abilities were re-evaluated with the same measures after a period of 18 to 24 months. Performance was compared to 12 children with IID pairwise matched for chronological age (CA) and non-verbal fluid reasoning (Gf) at Time 1. Non-verbal mental age (NVMA) was taken into account when delineating developmental trajectories. Results: Children with WS outperformed children with IID on expressive vocabulary development. In contrast, sentence comprehension was significantly poorer than in children with IID at the second time point. Increased variability and rather poor performance on pragmatic language tasks were demonstrated in the WS group. Irrelevant and off-topic extraneous information transfer continued to be a syndrome-specific characteristic of children with WS. Conclusion: The data provide new insights into diverging developmental trajectories across language domains. Expressive structural language skills tend to progress more rapidly than receptive language skills in children with WS causing more distinctive language profiles over time. Some children with WS seem to benefit from the growth in expressive structural language abilities to enhance their expressive pragmatic language skills, while in some others these abilities remain challenging. This study highlights the need for continued follow-up of language challenges in WS and for a dynamic and individualised interventional approach.

Longitudinal clustering provides a detailed yet comprehensible description of time profiles among subjects. With several approaches that are commonly used for this purpose, it remains unclear under which conditions a method is preferred over another method. We investigated the performance of five methods using Monte Carlo simulations on synthetic datasets, representing various scenarios involving polynomial time profiles. The performance was evaluated on two aspects: The agreement of the group assignment to the simulated reference, as measured by the split-join distance, and the trend estimation error, as measured by a weighted minimum of the mean squared error (WMMSE). Growth mixture modeling (GMM) was found to achieve the best overall performance, followed closely by a two-step approach using growth curve modeling and k-means (GCKM). Considering the model similarities between GMM and GCKM, the latter is preferred for large datasets for its computational efficiency. Longitudinal k-means (KML) and group-based trajectory modeling were found to have practically identical solutions in the case that the group trajectory model of the latter method is correctly specified. Both methods performed less than GMM and GCKM in most settings. [ABSTRACT FROM AUTHOR]