Cargando…
Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis
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-mak...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646805/ https://www.ncbi.nlm.nih.gov/pubmed/36351993 http://dx.doi.org/10.1038/s41598-022-23097-w |
_version_ | 1784827248559259648 |
---|---|
author | Hensen, Bennet Winkelmann, Carolin Wacker, Frank K. Vogt, Bodo Dewald, Cornelia L. A. Neumann, Thomas |
author_facet | Hensen, Bennet Winkelmann, Carolin Wacker, Frank K. Vogt, Bodo Dewald, Cornelia L. A. Neumann, Thomas |
author_sort | Hensen, Bennet |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9646805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96468052022-11-15 Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis Hensen, Bennet Winkelmann, Carolin Wacker, Frank K. Vogt, Bodo Dewald, Cornelia L. A. Neumann, Thomas Sci Rep Article 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. Nature Publishing Group UK 2022-11-09 /pmc/articles/PMC9646805/ /pubmed/36351993 http://dx.doi.org/10.1038/s41598-022-23097-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hensen, Bennet Winkelmann, Carolin Wacker, Frank K. Vogt, Bodo Dewald, Cornelia L. A. Neumann, Thomas Identification of Relevant Attributes for Liver Cancer Therapies (IRALCT): a maximum-difference-scaling analysis |
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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646805/ https://www.ncbi.nlm.nih.gov/pubmed/36351993 http://dx.doi.org/10.1038/s41598-022-23097-w |
work_keys_str_mv | AT hensenbennet identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis AT winkelmanncarolin identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis AT wackerfrankk identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis AT vogtbodo identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis AT dewaldcorneliala identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis AT neumannthomas identificationofrelevantattributesforlivercancertherapiesiralctamaximumdifferencescalinganalysis |