Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis
Abstract The Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT) project is intended to provide new insights into the relevant utility attributes regarding therapy choices for malignant primary and secondary liver tumors from the perspective of those who are involved in the dec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-23097-w |
| _version_ | 1811230401468825600 |
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| author | Bennet Hensen Carolin Winkelmann Frank K. Wacker Bodo Vogt Cornelia L. A. Dewald Thomas Neumann |
| author_facet | Bennet Hensen Carolin Winkelmann Frank K. Wacker Bodo Vogt Cornelia L. A. Dewald Thomas Neumann |
| author_sort | Bennet Hensen |
| collection | DOAJ |
| description | Abstract The Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT) project is intended to provide new insights into the relevant utility attributes regarding therapy choices for malignant primary and secondary liver tumors from the perspective of those who are involved in the decision-making process. It addresses the potential value of taking patients’ expectations and preferences into account during the decision-making and, when possible, adapting therapies according to these preferences. Specifically, it is intended to identify the relevant clinical attributes that influence the patients’, medical laymen’s, and medical professionals’ decisions and compare the three groups’ preferences. We conducted maximum difference (MaxDiff) scaling among 261 participants (75 physicians, 97 patients with hepatic malignancies, and 89 medical laymen) to rank the importance of 14 attributes previously identified through a literature review. We evaluated the MaxDiff data using count analysis and hierarchical Bayes estimation (HB). Physicians, patients, and medical laymen assessed the same 7 attributes as the most important: probability (certainty) of a complete removal of the tumor, probability of reoccurrence of the disease, pathological evidence of tumor removal, possible complications during the medical intervention, welfare after the medical intervention, duration and intensity of the pain, and degree of difficulty of the medical intervention. The cumulative relative importance of these 7 attributes was 88.3%. Our results show that the physicians’, patients’, and medical laymen’s preferences were very similar and stable. Trial registration DRKS-ID of the study: DRKS00013304, Date of Registration in DRKS: 2017/11/16. |
| first_indexed | 2024-04-12T10:29:59Z |
| format | Article |
| id | doaj.art-fef675a09ffd4e67971eb60c680a79d6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T10:29:59Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fef675a09ffd4e67971eb60c680a79d62022-12-22T03:36:54ZengNature PortfolioScientific Reports2045-23222022-11-0112111010.1038/s41598-022-23097-wIdentification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysisBennet Hensen0Carolin Winkelmann1Frank K. Wacker2Bodo Vogt3Cornelia L. A. Dewald4Thomas Neumann5Research Campus STIMULATE, Otto von Guericke University MagdeburgResearch Campus STIMULATE, Otto von Guericke University MagdeburgResearch Campus STIMULATE, Otto von Guericke University MagdeburgResearch Campus STIMULATE, Otto von Guericke University MagdeburgDepartment of Diagnostic and Interventional Radiology, Hannover Medical SchoolResearch Campus STIMULATE, Otto von Guericke University MagdeburgAbstract The Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT) project is intended to provide new insights into the relevant utility attributes regarding therapy choices for malignant primary and secondary liver tumors from the perspective of those who are involved in the decision-making process. It addresses the potential value of taking patients’ expectations and preferences into account during the decision-making and, when possible, adapting therapies according to these preferences. Specifically, it is intended to identify the relevant clinical attributes that influence the patients’, medical laymen’s, and medical professionals’ decisions and compare the three groups’ preferences. We conducted maximum difference (MaxDiff) scaling among 261 participants (75 physicians, 97 patients with hepatic malignancies, and 89 medical laymen) to rank the importance of 14 attributes previously identified through a literature review. We evaluated the MaxDiff data using count analysis and hierarchical Bayes estimation (HB). Physicians, patients, and medical laymen assessed the same 7 attributes as the most important: probability (certainty) of a complete removal of the tumor, probability of reoccurrence of the disease, pathological evidence of tumor removal, possible complications during the medical intervention, welfare after the medical intervention, duration and intensity of the pain, and degree of difficulty of the medical intervention. The cumulative relative importance of these 7 attributes was 88.3%. Our results show that the physicians’, patients’, and medical laymen’s preferences were very similar and stable. Trial registration DRKS-ID of the study: DRKS00013304, Date of Registration in DRKS: 2017/11/16.https://doi.org/10.1038/s41598-022-23097-w |
| spellingShingle | Bennet Hensen Carolin Winkelmann Frank K. Wacker Bodo Vogt Cornelia L. A. Dewald Thomas Neumann Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis Scientific Reports |
| title | Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
| title_full | Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
| title_fullStr | Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
| title_full_unstemmed | Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
| title_short | Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
| title_sort | identification of relevant attributes for liver cancer therapies iralct a maximum difference scaling analysis |
| url | https://doi.org/10.1038/s41598-022-23097-w |
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