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Patient feasibility as a novel approach for integrating IRT and LCA statistical models into patient-centric qualitative data--a pilot study.
- Source :
- Frontiers in Digital Health; 2024, p1-15, 15p
- Publication Year :
- 2024
-
Abstract
- Introduction: Clinical research increasingly recognizes the role and value of patient-centric data incorporation in trial design, aiming for more relevant, feasible, and engaging studies for participating patients. Despite recognition, research on analytical models regarding qualitative patient data analysis has been insufficient. Aim: This pilot study aims to explore and demonstrate the analytical framework of the "patient feasibility" concept--a novel approach for integrating patientcentric data into clinical trial design using psychometric latent class analysis (LCA) and interval response theory (IRT) models. Methods: A qualitative survey was designed to capture the diverse experiences and attitudes of patients in an oncological indication. Results were subjected to content analysis and categorization as a preparatory phase of the study. The analytical phase further employed LCA and hybrid IRT models to discern distinct patient subgroups and characteristics related to patient feasibility. Results: LCA identified three latent classes each with distinct characteristics pertaining to a latent trait defined as patient feasibility. Covariate analyses further highlighted subgroup behaviors. In addition, IRT analyses using the two-parameter logistic model, generalized partial credit model, and nominal response model highlighted further distinct characteristics of the studied group. The results provided insights into perceived treatment challenges, logistic challenges, and limiting factors regarding the standard of care therapy and clinical trial attitudes. [ABSTRACT FROM AUTHOR]
- Subjects :
- STATISTICAL models
DATA analysis
QUALITATIVE research
CAUSAL models
GLIOMAS
PILOT projects
CONTENT analysis
LOGISTIC regression analysis
EMPIRICAL research
STRUCTURAL equation modeling
CANCER patients
DESCRIPTIVE statistics
PATIENT-centered care
SURVEYS
THEMATIC analysis
PSYCHOMETRICS
RESEARCH
CONCEPTUAL structures
HEALTH outcome assessment
DATA analysis software
Subjects
Details
- Language :
- English
- Database :
- Complementary Index
- Journal :
- Frontiers in Digital Health
- Publication Type :
- Academic Journal
- Accession number :
- 180312343
- Full Text :
- https://doi.org/10.3389/fdgth.2024.1378497