2017
DOI: 10.1016/j.jpainsymman.2017.03.002
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Symptom Trajectories in Children Receiving Treatment for Leukemia: A Latent Class Growth Analysis With Multitrajectory Modeling

Abstract: This study is unique in that groups of patients with similar symptoms were identified rather than groups of symptoms. Further research using latent class growth analysis is needed.

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Cited by 67 publications
(74 citation statements)
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“…Because the three symptom measures (fatigue, pain, and nausea) were highly correlated with each other at each time point (Hockenberry et al, 2017), they were combined as a symptom cluster. through exploratory factor analysis with maximum likelihood estimation which returned a one-factor solution with significant factor loadings from .35 to .88 and at least 54% of variance explained at each time point (Table 14).…”
Section: Data Collectionmentioning
confidence: 99%
“…Because the three symptom measures (fatigue, pain, and nausea) were highly correlated with each other at each time point (Hockenberry et al, 2017), they were combined as a symptom cluster. through exploratory factor analysis with maximum likelihood estimation which returned a one-factor solution with significant factor loadings from .35 to .88 and at least 54% of variance explained at each time point (Table 14).…”
Section: Data Collectionmentioning
confidence: 99%
“…The first common approach was categorizing symptoms, which is a “variable-oriented” approach that focused on symptom variables ( 6 ). Most common statistical analysis, such as principal components analysis (PCA) ( 7 ), factor analysis (FA) ( 8 ), and cluster analysis ( 9 ) were used to identify symptom clusters in patients with lung cancer. Emotional or psychological and gastrointestinal symptom clusters were commonly identified.…”
Section: Introductionmentioning
confidence: 99%
“…The symptoms of fatigue, sleep disturbances, pain, nausea, and depression were evaluated by child self-report or parent proxy in 236 children. 3 Latent class growth analysis was used to categorize patients into mild, moderate, and severe symptom groups who followed distinct symptom trajectories over four intensive phases of ALL therapy. 3 In an earlier study, Buckner and colleagues conducted a latent profile analysis of children with cancer analyzing both patient-reported symptoms and functional outcomes.…”
Section: Introductionmentioning
confidence: 99%
“…3 Latent class growth analysis was used to categorize patients into mild, moderate, and severe symptom groups who followed distinct symptom trajectories over four intensive phases of ALL therapy. 3 In an earlier study, Buckner and colleagues conducted a latent profile analysis of children with cancer analyzing both patient-reported symptoms and functional outcomes. They noted that evaluating functional impairments in relation to symptoms is essential to understanding how cancer impacts the child’s quality of life.…”
Section: Introductionmentioning
confidence: 99%