Comparing Generic Paediatric Health-Related Quality-of-Life Instruments: A Dimensionality Assessment Using Factor

Mina Bahrampour1, Renee Jones2, Kim Dalziel2

  • 1Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney, Sydney, NSW, Australia. mina.bahrampour@uts.edu.au.

Pharmacoeconomics
|May 31, 2024
PubMed

Insights

This study compared paediatric health-related quality of life (HRQoL) instruments. Exploratory factor analysis revealed distinct underlying structures, with self-reports identifying an additional social functioning domain compared to proxy reports.

Area of Science:

  • Paediatric Health Outcomes Research
  • Psychometrics
  • Health Services Research

Background:

  • Widely used paediatric health-related quality of life (HRQoL) instruments include EQ-5D-Y-5L, CHU-9D, PedsQL, and HUI.
  • Limited empirical evidence exists on the relationships between items within these instruments and an overarching HRQoL model.

Purpose of the Study:

  • To explore the dimensionality of common paediatric HRQoL instruments using exploratory factor analysis (EFA).
  • To compare the underlying domain structures of EQ-5D-Y-5L, CHU-9D, PedsQL, and HUI.

Main Methods:

  • Utilized data from the Australian Paediatric Multi-Instrument Comparison (P-MIC) Study.
  • Conducted EFA on pooled items from four paediatric HRQoL instruments (EQ-5D-Y-5L, CHU-9D, PedsQL, HUI) using self- and proxy-reported data.
  • Determined factor structure using eigenvalues > 1, a correlation cut-off of 0.32 for item loading, and oblique rotation.

Main Results:

  • Proxy-reported data yielded a six-factor structure: emotional functioning, pain, daily activities, physical functioning, school functioning, and senses.
  • Self-reported data revealed a similar seven-factor structure, with social functioning identified as an additional domain.

Conclusions:

  • Provided evidence for both similarities and differences in what paediatric HRQoL instruments measure.
  • Identified subtle discrepancies between self- and proxy-reported data regarding item relationships within identified domains.
Abstract

Related Concept Videos

Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
316
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177