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
Qual Life Res
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
Jolanda J. Kossakowski 0 1 2
Sacha Epskamp 0 1 2
Jacobien M. Kieffer 0 1 2
Claudia D. van Borkulo 0 1 2
Mijke Rhemtulla 0 1 2
Denny Borsboom 0 1 2
0 Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen , Hanzeplein 1, 9700 RB Groningen , The Netherlands
1 Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute , Plesmanlaan 121, 1066 CX Amsterdam , The Netherlands
2 & Jolanda J. Kossakowski
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 (SF36). 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.
Health-Related Quality of Life; Cancer; Network analysis; Psychometrics; Short Form Health; Survey; SF-36
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Department of Psychology, University of Amsterdam,
Weesperplein 4, 1018 WZ Amsterdam, The Netherlands
Introduction
The question of how theoretical constructs like
HealthRelated Quality of Life (HRQoL) should be related to
observables reflects one of the fundamental scientific issues
facing any field: how should we think about the relation
between constructs and observables? Two dominant
approaches to this question are known as formative and
reflective modeling [
1, 2
]. In formative models (FMs),
items are viewed as causes of the theoretical construct
under consideration, whereas in reflective measurement
models (RMMs), items are seen as effects of that construct.
In the present paper, we argue that neither of these
approaches suits HRQoL, and present an alternative
approach based on a network model (NM).
Some of the analyses performed in HRQoL research
have been based on the application of FMs using principal
components analysis (PCA), creating weighted composites
of observables to achieve data reduction [
3
]. The 36-item
Short Form Health Survey (SF-36), a commonly used
instrument across different disease conditions and patient
groups [
4
], has been developed on the basis of PCA. In an
FM, HRQoL is the common effect of items (or simply a
composite score formed out of them, like in PCA [
5
]). The
idea behind the FM is that observed variables contribute to
HRQoL: if the observables change, HRQoL changes as a
result. A simplified example of the FM is represented in
Fig. 1a where the observables are represented as forming a
‘‘mental health’’ (MH) component, one of the domains of
the SF-36.
An advantage of the FM is that it allows people with
similar levels of HRQoL to have different item responses.
For example, John may have a poor HRQoL because he is a
very nervous person, whereas Jane may have a poor
HRQoL because she feels downhearted and blue.
Furthermore, the FM is appropriate when one would like to
calculate a single score to represent someone’s HRQoL,
which can be used as an index of general functioning.
However, the FM also has some downsides. First, the FM
is unidentified unless external outcome variables are added
to identify its parameters [
6
]. Since different external
variables yield different modeling solutions, the definition
of a formative construct cannot be assumed stable across
applications (i.e., interpretational confounding; [
7
]).
Second, since the FM does not have implications for the
correlation structure between item responses, it cannot
evaluate important relations between items that make up
HRQoL, nor the processes that give rise to the correlation
structure that characterizes it [
1
]: relations between items
are modeled as nuisance, even when they may harbor
important information.
An alternative to the FM [e.g., 8, 9] is the RMM. In an
RMM, HRQoL is defined as the common determinant of
item responses. For example, Fig. 1b shows that the items
NP, DC, CP, DP and HP have a common determinant,
namely MH. When using an RMM, one has to satisf (...truncated)