The application of a network approach to Health-Related Quality of Life (HRQoL): introducing a new method for assessing HRQoL in healthy adults and cancer patients

• Published in: Quality of life research Vol. 25; no. 4; pp. 781 - 792
• Main Authors: Kossakowski, Jolanda J., Epskamp, Sacha, Kieffer, Jacobien M., van Borkulo, Claudia D., Rhemtulla, Mijke, Borsboom, Denny
• Format: Journal Article
• Language: English
• Published: Cham Springer 01.04.2016; Springer International Publishing; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Cancer; Emotional problems; Epidemiology; Female; Health Status; Health surveys; Humans; Male; Medicine; Medicine & Public Health; Mental health; METHODS; Middle Aged; Neoplasms; Network analysis; Patients; Physical health; Principal components analysis; Psychometrics; Public Health; Quality of Life; Quality of Life Research; Quantitative psychology; Serenity; Sociology; Studies; Surveys and Questionnaires; Variables; Vitality; Short Form Health Survey; Psychometrics; Network analysis; Health-Related Quality of Life; Cancer; SF-36
• ISSN: 0962-9343; 1573-2649
• DOI: 10.1007/s11136-015-1127-z

Purpose Health-Related Quality of Life (HRQoL) research has typically adopted either a formative approach, in which HRQoL is the common effect of its observables, or a reflective approach—defining HRQoL as a latent variable that determines observable characteristics of HRQoL. Both approaches, however, do not take into account the complex organization of these characteristics. The objective of this study was to introduce a new approach for analyzing HRQoL data, namely a network model (NM). An NM, as opposed to traditional research strategies, accounts for interactions among observables and offers a complementary analytic approach. Methods We applied the NM to samples of Dutch cancer patients (N = 485) and Dutch healthy adults (N = 1742) who completed the 36-item Short Form Health Survey (SF-36). Networks were constructed for both samples separately and for a combined sample with diagnostic status added as an extra variable. We assessed the network structures and compared the structures of the two separate samples on the item and domain levels. The relative importance of individual items in the network structures was determined using centrality analyses. Results We found that the global structure of the SF-36 is dominant in all networks, supporting the validity of questionnaire's subscales. Furthermore, results suggest that the network structure of both samples was highly similar. Centrality analyses revealed that maintaining a daily routine despite one's physical health predicts HRQoL levels best. Conclusions We concluded that the NM provides a fruitful alternative to classical approaches used in the psychometric analysis of HRQoL data.