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An Exploratory Study on Using Principal-Component Analysis and Confirmatory Factor Analysis to Identify Bolt-On
Aureliano Paolo Finch1, John Edward Brazier1, Clara Mukuria1
1Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK.
Summary
Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) can identify missing dimensions for generic health measures like the EQ-5D. These statistical methods help find suitable bolt-ons to improve generic measures for specific conditions.
Area of Science:
- Health Economics
- Psychometrics
- Statistical Modeling
Background:
- Generic preference-based measures, such as the EuroQol five-dimensional questionnaire (EQ-5D), are crucial for economic evaluations.
- However, their core descriptive systems may not capture all relevant health dimensions for specific conditions.
- Expanding these measures with 'bolt-ons' is a potential solution, but current review-based methods lack precision in identifying necessary dimensions.
Purpose of the Study:
- To explore the utility of Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) for identifying appropriate bolt-on dimensions for the EQ-5D.
- To determine if PCA and CFA can systematically identify dimensions not covered by the current EQ-5D descriptive system.
Main Methods:
- Utilized data from the international Multi-Instrument Comparison study, an online survey of health and well-being measures across five countries.
- Applied PCA to investigate the underlying dimensional structure of 92 items from nine instruments.
- Employed CFA to confirm the identified structure and cross-validated the model in random sample halves.
Main Results:
- PCA indicated a nine-component solution, which was subsequently confirmed by CFA.
- Identified dimensions included those covered by the EQ-5D (psychological symptoms, physical functioning, pain) and those not covered (satisfaction, speech/cognition, relationships, hearing, vision, energy/sleep).
- The uncovered dimensions represent potential candidate bolt-ons for the EQ-5D.
Conclusions:
- Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) are effective statistical techniques for identifying potential bolt-on dimensions.
- These methods offer a more systematic approach to expanding generic health measures compared to review-based methods.
- The identified dimensions provide a basis for developing improved, condition-specific versions of the EQ-5D.
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