Trial reportSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2014
Item response theory analysis of the patient satisfaction with cancer-related care measure: a psychometric investigation in a multicultural sample of 1,296 participants.
Trial report in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Trial
- Assessing the Generalizability of Client Experience Measurement Tools in Low- and Middle-Income Countries: A Narrative Review.Global health, science and practice · 2025Review
- Measuring Team Functioning During the COVID-19 Pandemic: Perspectives of Cancer Care Team Members.Journal of multidisciplinary healthcare · 2024Article
- Patient Satisfaction with Navigator Interpersonal Relationship (PSN-I): item-level psychometrics using IRT analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2020Article
- A Comparative Study of Liver Disease Care in the USA and Urban and Rural China.Digestive diseases and sciences · 2016Article
- Which kind of psychometrics is adequate for patient satisfaction questionnaires?Patient preference and adherence · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWe developed and validated a Patient Satisfaction with Cancer-Related Care (PSCC) measure using classical test theory methods. The present study applied item response theory (IRT) analysis to determine item-level psychometric properties, facilitate development of short forms, and inform future applications for the PSCC.
methodsWe applied unidimensional IRT models to PSCC data from 1,296 participants (73% female; 18 to 86 years). An unconstrained graded response model (GRM) and a Rasch Model were fitted to estimate indices for model comparison using likelihood ratio (LR) test and information criteria. We computed item and latent trait parameter estimates, category and operating characteristic curves, and tested information curves for the better fitting model.
resultsThe GRM fitted the data better than the Rasch Model (LR = 828, df = 17, p < 0.001). The log-likelihood (-17,390.38 vs. -17,804.26) was larger, and the AIC and BIC were smaller for the GRM compared to the Rash Model (AIC = 34,960.77 vs. 35,754.73; BIC = 35,425.80 vs. 36,131.92). Item parameter estimates (IPEs) showed substantial variation in items' discriminating power (0.94 to 2.18). Standard errors of the IPEs were small (threshold parameters mostly around 0.1; discrimination parameters 0.1 to 0.2), confirming the precision of the IPEs.
conclusionThe GRM provides precise IPEs that will enable comparable scores from different subsets of items, and facilitate optimal selections of items to estimate patients' latent satisfaction level. Given the large calibration sample, the IPEs can be used in settings with limited resources (e.g., smaller samples) to estimate patients' satisfaction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.