Psychometric Properties and Interpretability of PRO-CTCAE® Average Composite Scores as a Summary Metric of Symptomatic Adverse Event Burden Article Swipe
Minji K. Lee
,
Sandra A. Mitchell
,
Ethan Basch
,
Allison M. Deal
,
Blake Langlais
,
Gita Thanarajasingam
,
Brenda Ginos
,
Lauren Rogak
,
Tito R. Mendoza
,
Antonia V. Bennett
,
Brie N. Noble
,
Gina L. Mazza
,
Amylou C. Dueck
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/cancers17213459
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/cancers17213459
Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/cancers17213459
- https://www.mdpi.com/2072-6694/17/21/3459/pdf?version=1761658158
- OA Status
- gold
- References
- 39
- OpenAlex ID
- https://openalex.org/W4415625774
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https://openalex.org/W4415625774Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/cancers17213459Digital Object Identifier
- Title
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Psychometric Properties and Interpretability of PRO-CTCAE® Average Composite Scores as a Summary Metric of Symptomatic Adverse Event BurdenWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-28Full publication date if available
- Authors
-
Minji K. Lee, Sandra A. Mitchell, Ethan Basch, Allison M. Deal, Blake Langlais, Gita Thanarajasingam, Brenda Ginos, Lauren Rogak, Tito R. Mendoza, Antonia V. Bennett, Brie N. Noble, Gina L. Mazza, Amylou C. DueckList of authors in order
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https://doi.org/10.3390/cancers17213459Publisher landing page
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https://www.mdpi.com/2072-6694/17/21/3459/pdf?version=1761658158Direct link to full text PDF
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- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-6694/17/21/3459/pdf?version=1761658158Direct OA link when available
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| abstract_inverted_index.at | 24 |
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| abstract_inverted_index.Mean | 119 |
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| abstract_inverted_index.This | 31 |
| abstract_inverted_index.both | 241 |
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