2016
DOI: 10.3414/me16-01-0035
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A Factor Analysis Approach for Clustering Patient Reported Outcomes

Abstract: Summary Background In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identif… Show more

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Cited by 11 publications
(3 citation statements)
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“…This can help show consistent factors causing yield gaps over a larger spatial scale and guide policy interventions to enhance yields. The method has been used in clinical studies to obtain general conclusions on clinical conditions (Oh et al, 2016;Ohshiro & Ueda, 2018), but its use in agricultural studies is still low. Combining different methods to examine factors influencing maize yield gaps can provide complementary findings that are relevant at different spatial scales in smallholder farming systems, improving yield gap studies.…”
Section: Introductionmentioning
confidence: 99%
“…This can help show consistent factors causing yield gaps over a larger spatial scale and guide policy interventions to enhance yields. The method has been used in clinical studies to obtain general conclusions on clinical conditions (Oh et al, 2016;Ohshiro & Ueda, 2018), but its use in agricultural studies is still low. Combining different methods to examine factors influencing maize yield gaps can provide complementary findings that are relevant at different spatial scales in smallholder farming systems, improving yield gap studies.…”
Section: Introductionmentioning
confidence: 99%
“…FA is a statistical method to investigate the relationship between items in a dataset. By combining explorative and confirmatory FA clinically relevant factors might be identified [67]. However, it is also known that FA is a large-sample procedure; generalizable or replicable results are unlikely if the sample is too small [68].…”
Section: Discussionmentioning
confidence: 99%
“…In symptom clusters research, exploratory factor analysis (EFA) has been a preferred approach for detecting clusters of symptoms that may not be expected based on traditional diagnostic groupings or severity delineations (Harris et al, 2022). Considered one of several "variable-centered approaches" of symptom clusters research (in contrast to other "patient-centered approaches," such as latent profile analysis), EFA allows for statistical understanding of how an array of symptoms can be grouped to reveal the underlying structure of symptoms and how they may be related or caused by shared etiologies (Barsevick, 2016;Harris et al, 2022;Oh et al, 2016;Skerman et al, 2012). In the psychometric and neuropsychological assessment traditions, EFA is often used for identifying "domains" for assessment, for psychometric validation of tests or batteries, or for further interpretation of assessments, such as developing composite scores (Ma et al, 2021;Strauss & Fritsch, 2004).…”
mentioning
confidence: 99%