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Validating online approaches for rare disease research using latent class mixture modeling
BACKGROUND: Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not re...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108361/ https://www.ncbi.nlm.nih.gov/pubmed/33971926 http://dx.doi.org/10.1186/s13023-021-01827-z |
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author | Dwyer, Andrew A. Zeng, Ziwei Lee, Christopher S. |
author_facet | Dwyer, Andrew A. Zeng, Ziwei Lee, Christopher S. |
author_sort | Dwyer, Andrew A. |
collection | PubMed |
description | BACKGROUND: Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not represent the broader patient population—as patients recruited from patient organizations are thought to have high levels of needs. We applied latent class mixture modeling (LCMM) to define patient clusters based on underlying characteristics. We used previously collected data from a cohort of patients with congenital hypogonadotropic hypogonadism who were recruited online in collaboration with a patient organization. Patient demographics, clinical information, Revised Illness Perception Questionnaire (IPQ-R) scores and Zung self-rating depression Scale (SDS) were used as variables for LCMM analysis. Specifically, we aimed to test the classic critique that patients recruited online in collaboration with a patient organization are a homogenous group with high needs. We hypothesized that distinct classes (clinical profiles) of patients could be identified—thereby demonstrating the validity of online recruitment and supporting transferability of findings. RESULTS: In total, 154 patients with CHH were included. The LCMM analysis identified three distinct subgroups (Class I: n = 84 [54.5%], Class II: n = 41 [26.6%], Class III: n = 29 [18.8%]) that differed significantly in terms of age, education, disease consequences, emotional consequences, illness coherence and depression symptoms (all p < 0.001) as well as age at diagnosis (p = 0.045). Classes depict a continuum of psychosocial impact ranging from severe to relatively modest. Additional analyses revealed later diagnosis (Class I: 19.2 ± 6.7 years [95% CI 17.8–20.7]) is significantly associated with worse psychological adaptation and coping as assessed by disease consequences, emotional responses, making sense of one’s illness and SDS depressive symptoms (all p < 0.001). CONCLUSIONS: We identify three distinct classes of patients who were recruited online in collaboration with a patient organization. Findings refute prior critiques of patient partnership and web-based recruitment for rare disease research. This is the first empirical data suggesting negative psychosocial sequelae of later diagnosis (“diagnostic odyssey”) often observed in CHH. |
format | Online Article Text |
id | pubmed-8108361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81083612021-05-11 Validating online approaches for rare disease research using latent class mixture modeling Dwyer, Andrew A. Zeng, Ziwei Lee, Christopher S. Orphanet J Rare Dis Research BACKGROUND: Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not represent the broader patient population—as patients recruited from patient organizations are thought to have high levels of needs. We applied latent class mixture modeling (LCMM) to define patient clusters based on underlying characteristics. We used previously collected data from a cohort of patients with congenital hypogonadotropic hypogonadism who were recruited online in collaboration with a patient organization. Patient demographics, clinical information, Revised Illness Perception Questionnaire (IPQ-R) scores and Zung self-rating depression Scale (SDS) were used as variables for LCMM analysis. Specifically, we aimed to test the classic critique that patients recruited online in collaboration with a patient organization are a homogenous group with high needs. We hypothesized that distinct classes (clinical profiles) of patients could be identified—thereby demonstrating the validity of online recruitment and supporting transferability of findings. RESULTS: In total, 154 patients with CHH were included. The LCMM analysis identified three distinct subgroups (Class I: n = 84 [54.5%], Class II: n = 41 [26.6%], Class III: n = 29 [18.8%]) that differed significantly in terms of age, education, disease consequences, emotional consequences, illness coherence and depression symptoms (all p < 0.001) as well as age at diagnosis (p = 0.045). Classes depict a continuum of psychosocial impact ranging from severe to relatively modest. Additional analyses revealed later diagnosis (Class I: 19.2 ± 6.7 years [95% CI 17.8–20.7]) is significantly associated with worse psychological adaptation and coping as assessed by disease consequences, emotional responses, making sense of one’s illness and SDS depressive symptoms (all p < 0.001). CONCLUSIONS: We identify three distinct classes of patients who were recruited online in collaboration with a patient organization. Findings refute prior critiques of patient partnership and web-based recruitment for rare disease research. This is the first empirical data suggesting negative psychosocial sequelae of later diagnosis (“diagnostic odyssey”) often observed in CHH. BioMed Central 2021-05-10 /pmc/articles/PMC8108361/ /pubmed/33971926 http://dx.doi.org/10.1186/s13023-021-01827-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Dwyer, Andrew A. Zeng, Ziwei Lee, Christopher S. Validating online approaches for rare disease research using latent class mixture modeling |
title | Validating online approaches for rare disease research using latent class mixture modeling |
title_full | Validating online approaches for rare disease research using latent class mixture modeling |
title_fullStr | Validating online approaches for rare disease research using latent class mixture modeling |
title_full_unstemmed | Validating online approaches for rare disease research using latent class mixture modeling |
title_short | Validating online approaches for rare disease research using latent class mixture modeling |
title_sort | validating online approaches for rare disease research using latent class mixture modeling |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108361/ https://www.ncbi.nlm.nih.gov/pubmed/33971926 http://dx.doi.org/10.1186/s13023-021-01827-z |
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