Browsing by Subject "Cluster analysis"

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    Achievement goal profiles, trait-anxiety and state-emotion of young female competitive horse riders
    (2011) Duff-Riddell, Caroline; Louw, Johann
    The goal orientations of female riders (N=83) between the ages of 9 and 20 were investigated with a view to extracting goal profiles from the collected data. Goal orientations were identified by means of the Achievement Goal Questionnaire for Sport (AGQ-S), which is based in the 2x2 achievement goal model. Goal profiles were created using cluster analysis. Seven distinct goal profiles emerged from the data. The goal profiles were compared to measures of the rider's trait-anxiety and state-emotion in competitive horse riding. The profile that was high in the approach orientations and low in the avoidant orientations emerged as the most emotionally robust profile. It was also the most competitively successful profile. The profiles where the avoidant orientations were high emerged as the most emotionally vulnerable profiles. Furthermore, they did not demonstrate any particular competitive success.
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    Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
    (2021-10-24) de Vries, Petrus J.; Leclezio, Loren; Gardner-Lubbe, Sugnet; Krueger, Darcy; Sahin, Mustafa; Sparagana, Steven; De Waele, Liesbeth; Jansen, Anna
    Background Tuberous Sclerosis Complex (TSC), a multi-system genetic disorder, is associated with a wide range of TSC-Associated Neuropsychiatric Disorders (TAND). Individuals have apparently unique TAND profiles, challenging diagnosis, psycho-education, and intervention planning. We proposed that identification of natural TAND clusters could lead to personalized identification and treatment of TAND. Two small-scale studies showed cluster and factor analysis could identify clinically meaningful natural TAND clusters. Here we set out to identify definitive natural TAND clusters in a large, international dataset. Method Cross-sectional, anonymized TAND Checklist data of 453 individuals with TSC were collected from six international sites. Data-driven methods were used to identify natural TAND clusters. Mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Statistical robustness of clusters was evaluated with 1000-fold bootstrapping, and internal consistency calculated with Cronbach’s alpha. Results Ward’s method rendered seven natural TAND clusters with good robustness on bootstrapping. Cluster analysis showed significant convergence with an exploratory factor analysis solution, and, with the exception of one cluster, internal consistency of the emerging clusters was good to excellent. Clusters showed good clinical face validity. Conclusions Our findings identified a data-driven set of natural TAND clusters from within highly variable TAND Checklist data. The seven natural TAND clusters could be used to train families and professionals and to develop tailored approaches to identification and treatment of TAND. Natural TAND clusters may also have differential aetiological underpinnings and responses to molecular and other treatments.
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    Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project
    (2020-09-01) de Vries, Petrus J; Belousova, Elena; Benedik, Mirjana P; Carter, Tom; Cottin, Vincent; Curatolo, Paolo; D’Amato, Lisa; Beure d’Augères, Guillaume; Ferreira, José C; Feucht, Martha; Fladrowski, Carla; Hertzberg, Christoph; Jozwiak, Sergiusz; Lawson, John A; Macaya, Alfons; Marques, Ruben; Nabbout, Rima; O’Callaghan, Finbar; Qin, Jiong; Sander, Valentin; Sauter, Matthias; Shah, Seema; Takahashi, Yukitoshi; Touraine, Renaud; Youroukos, Sotiris; Zonnenberg, Bernard; Kingswood, J. Chris; Jansen, Anna C
    Abstract Background Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study. Methods As part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters. Results A total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions. Conclusions Results from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters.
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    Phenotypes of adults with congenital heart disease around the globe: a cluster analysis
    (2021-02-10) Callus, Edward; Pagliuca, Silvana; Boveri, Sara; Ambrogi, Federico; Luyckx, Koen; Kovacs, Adrienne H; Apers, Silke; Budts, Werner; Enomoto, Junko; Sluman, Maayke A; Wang, Jou-Kou; Jackson, Jamie L; Khairy, Paul; Cook, Stephen C; Chidambarathanu, Shanthi; Alday, Luis; Eriksen, Katrine; Dellborg, Mikael; Berghammer, Malin; Johansson, Bengt; Mackie, Andrew S; Menahem, Samuel; Caruana, Maryanne; Veldtman, Gruschen; Soufi, Alexandra; Fernandes, Susan M; White, Kamila; Kutty, Shelby; Moons, Philip
    Objective To derive cluster analysis-based groupings for adults with congenital heart disease (ACHD) when it comes to perceived health, psychological functioning, health behaviours and quality of life (QoL). Methods This study was part of a larger worldwide multicentre study called APPROACH-IS; a cross sectional study which recruited 4028 patients (2013–2015) from 15 participating countries. A hierarchical cluster analysis was performed using Ward's method in order to group patients with similar psychological characteristics, which were defined by taking into consideration the scores of the following tests: Sense Of Coherence, Health Behavior Scale (physical exercise score), Hospital Anxiety Depression Scale, Illness Perception Questionnaire, Satisfaction with Life Scale and the Visual Analogue Scale scores of the EQ-5D perceived health scale and a linear analogue scale (0–100) measuring QoL. Results 3768 patients with complete data were divided into 3 clusters. The first and second clusters represented 89.6% of patients in the analysis who reported a good health perception, QoL, psychological functioning and the greatest amount of exercise. Patients in the third cluster reported substantially lower scores in all PROs. This cluster was characterised by a significantly higher proportion of females, a higher average age the lowest education level, more complex forms of congenital heart disease and more medical comorbidities. Conclusions This study suggests that certain demographic and clinical characteristics may be linked to less favourable health perception, quality of life, psychological functioning, and health behaviours in ACHD. This information may be used to improve psychosocial screening and the timely provision of psychosocial care.