Current Methodological Practices to Define Within-Patient Meaningful Change in Rare Diseases: A Targeted Literature Review
Author(s)
Shaurya Deep Bajwa, MBA, M.Sc.1, VATSAL CHHAYA, M.Sc.2, KAPIL KHAMBHOLJA, PhD2.
1Catalyst Clinical Research, Thiruvananthapuram, India, 2Catalyst Clinical Research, Baroda, India.
1Catalyst Clinical Research, Thiruvananthapuram, India, 2Catalyst Clinical Research, Baroda, India.
OBJECTIVES: Measuring meaningful change in rare diseases is challenging due to small sample sizes, phenotypic heterogeneity, and lack of validated endpoints. Despite growing emphasis on patient-centred evidence, guidance on clinical outcome assessment (COA) strategies tailored to rare conditions remains limited. This study aims to evaluate current methodological practices used to define meaningful within-individual change (MWIC) in rare disease clinical research and identify trends in statistical approaches applied to COA development and MWIC measurement.
METHODS: A targeted literature review was performed in PubMed (2020-2025), including English-language clinical trials, systematic reviews, meta-analyses, and observational studies. Studies relevant to rare diseases and COA methodologies, including MWIC definitions and statistical design elements (e.g., anchor-based, distribution-based methods), were screened using keywords such as “rare disease,” “meaningful change,” and “MWIC.” A two-stage screening process following PRISMA guidelines was applied, and eligible full texts were reviewed for data extraction.
RESULTS: From 1,947 records, 142 rare disease-specific studies were analyzed. Of these, 22% (31/142) reported COA endpoints, and 11% (16/142) applied statistical methods adapted for small, heterogeneous populations. Anchor-based methods were used in 35% (11/31), distribution-based in 21% (7/31), and both in 9% (3/31). PROMIS tools appeared in 28% (9/31), though few included rare disease-specific validation.Key methodological limitations included inadequate handling of small samples in 47% (15/31), limited use of mixed-effects or Bayesian models (<12%, 3/31), and scarce phenotype subgroup analysis (19%, 6/31). Only 10% (3/31) incorporated patient or caregiver input. Use of real-world evidence and federated data strategies was minimal (<5%, 1/31).
CONCLUSIONS: Our study highlighted significant gaps in COA development for rare diseases, including limited use of advanced statistical methods, inadequate adaptation to small, heterogeneous populations, and minimal integration of patient input or real-world data. Addressing these gaps requires greater application of flexible modeling, stakeholder engagement, and innovative data strategies to support valid, meaningful MWIC definitions in rare disease research.
METHODS: A targeted literature review was performed in PubMed (2020-2025), including English-language clinical trials, systematic reviews, meta-analyses, and observational studies. Studies relevant to rare diseases and COA methodologies, including MWIC definitions and statistical design elements (e.g., anchor-based, distribution-based methods), were screened using keywords such as “rare disease,” “meaningful change,” and “MWIC.” A two-stage screening process following PRISMA guidelines was applied, and eligible full texts were reviewed for data extraction.
RESULTS: From 1,947 records, 142 rare disease-specific studies were analyzed. Of these, 22% (31/142) reported COA endpoints, and 11% (16/142) applied statistical methods adapted for small, heterogeneous populations. Anchor-based methods were used in 35% (11/31), distribution-based in 21% (7/31), and both in 9% (3/31). PROMIS tools appeared in 28% (9/31), though few included rare disease-specific validation.Key methodological limitations included inadequate handling of small samples in 47% (15/31), limited use of mixed-effects or Bayesian models (<12%, 3/31), and scarce phenotype subgroup analysis (19%, 6/31). Only 10% (3/31) incorporated patient or caregiver input. Use of real-world evidence and federated data strategies was minimal (<5%, 1/31).
CONCLUSIONS: Our study highlighted significant gaps in COA development for rare diseases, including limited use of advanced statistical methods, inadequate adaptation to small, heterogeneous populations, and minimal integration of patient input or real-world data. Addressing these gaps requires greater application of flexible modeling, stakeholder engagement, and innovative data strategies to support valid, meaningful MWIC definitions in rare disease research.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
CO67
Topic
Clinical Outcomes, Study Approaches
Topic Subcategory
Clinical Outcomes Assessment
Disease
Rare & Orphan Diseases