Overview of Rare Disease Patient-Centered Studies Methodologies and Recommendations to Overcome Challenges
Author(s)
Zhen Dong, PhD1, Shiau-Han Chen, MSc2, Xiahong Zhao, PhD1, Yvonne Lee, MPH, PhD1.
1IQVIA Solutions Asia Pte. Ltd, Singapore, Singapore, 2IQVIA Solutions Taiwan Ltd, Taipei, Taiwan.
1IQVIA Solutions Asia Pte. Ltd, Singapore, Singapore, 2IQVIA Solutions Taiwan Ltd, Taipei, Taiwan.
OBJECTIVES: Patient-centered studies (PCS) are crucial for incorporating patient perspectives in rare diseases (RD). However, RD pose unique challenges due to their complex patient populations. This study aims to explore and identify suitable methodologies for PCS in RD to address these complexities.
METHODS: A targeted literature search was conducted on PubMed using generative artificial intelligence to identify studies in Australia, China, Hong Kong, Korea, Japan, Singapore, and Taiwan (2020-2025) with the keywords related to RD, PCS and patient-centric. Predefined prompts and large language models (Qwen2 models) were applied to generate structured outputs, which were used as filters in Microsoft Excel to exclude reviews, meta-analyses, and case reports. Study objectives, populations, methodologies, and limitations were extracted and analyzed using narrative synthesis.
RESULTS: Among 72 identified publications, 46 were included for analysis. Over a third of PCS were found to understand knowledge, attitude, and practice (38.6%), followed by disease burden (35.1%), financial burden (15.8%), and therapeutic options (10.5%). Quantitative methods (69.6%) predominated, followed by qualitative (17.4%), mixed methods (8.7%), preference studies (4.3%), and digital solutions (2.2%). Common limitation across methodologies included limited sample size, selection bias, and high heterogeneity, with various solutions implemented. First, collaboration with patient groups, arranging patient advocacy or leveraging social media could supplement qualitative concept elicitation. Second, combining populations with similar disease presentations could increase the sample size for evaluating quality of life. Third, stratified sampling methods could mitigate selection bias. Fourth, developing a tailored, multi-attribute questionnaire can enhance applicability of outcomes across heterogeneous RD manifestations. Lastly, deploying a mixed method could provide an in-depth understanding of humanistic burden of RD.
CONCLUSIONS: This study presents a comprehensive overview of methodologies for PCS in RD, emphasizing solutions to overcome inherent challenges. While quantitative approaches prevail, researchers are encouraged to adopt fit-for-purpose designs to enhance the robustness and relevance of PCS in RD.
METHODS: A targeted literature search was conducted on PubMed using generative artificial intelligence to identify studies in Australia, China, Hong Kong, Korea, Japan, Singapore, and Taiwan (2020-2025) with the keywords related to RD, PCS and patient-centric. Predefined prompts and large language models (Qwen2 models) were applied to generate structured outputs, which were used as filters in Microsoft Excel to exclude reviews, meta-analyses, and case reports. Study objectives, populations, methodologies, and limitations were extracted and analyzed using narrative synthesis.
RESULTS: Among 72 identified publications, 46 were included for analysis. Over a third of PCS were found to understand knowledge, attitude, and practice (38.6%), followed by disease burden (35.1%), financial burden (15.8%), and therapeutic options (10.5%). Quantitative methods (69.6%) predominated, followed by qualitative (17.4%), mixed methods (8.7%), preference studies (4.3%), and digital solutions (2.2%). Common limitation across methodologies included limited sample size, selection bias, and high heterogeneity, with various solutions implemented. First, collaboration with patient groups, arranging patient advocacy or leveraging social media could supplement qualitative concept elicitation. Second, combining populations with similar disease presentations could increase the sample size for evaluating quality of life. Third, stratified sampling methods could mitigate selection bias. Fourth, developing a tailored, multi-attribute questionnaire can enhance applicability of outcomes across heterogeneous RD manifestations. Lastly, deploying a mixed method could provide an in-depth understanding of humanistic burden of RD.
CONCLUSIONS: This study presents a comprehensive overview of methodologies for PCS in RD, emphasizing solutions to overcome inherent challenges. While quantitative approaches prevail, researchers are encouraged to adopt fit-for-purpose designs to enhance the robustness and relevance of PCS in RD.
Conference/Value in Health Info
2025-09, ISPOR Real-World Evidence Summit 2025, Tokyo, Japan
Value in Health Regional, Volume 49S (September 2025)
Code
RWD218
Topic Subcategory
Distributed Data & Research Networks
Disease
SDC: Rare & Orphan Diseases