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

This study involves a secondary analysis of data that were originally gathered for the International Quality of Life Assessment Project (IQOLA) and have been described in detail in a previous paper [20]. The data involved were unidentifiable; it could not be traced back to the individual. Therefore, we did not require informed consent. The present study focuses on two subsamples of the IQOLA project: Dutch cancer patients (cancer patient sample) and a Dutch nationwide sample of adults who were not diagnosed with cancer (national sample). Participants completed the SF-36 between 1992 and 1994 (cancer patient sample) or in 1996 (national sample). In addition, we combined the two datasets (combined sample) to analyze the function of diagnostic status (i.e., the distinction between being diagnosed with cancer or not) in the HRQoL system. To this end, we added diagnostic status as a separate variable in the network structure.

The SF-36 [21, 22] is a HRQoL questionnaire that has been adapted and translated into more than ten languages over the past few decades, and has also been validated in various patient groups and languages [23‐25]. The SF-36 is based on an FM and comprises the following eight first-order latent variables (domains): physical functioning (PF), role limitations–physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitations–emotional (RE) and mental health (MH). These domains are themselves modeled as the cause of two second-order latent variables [26], which are represented by physical and mental component summary scores (PCS and MCS, respectively). Domain scores were calculated by summing up item responses, after which the scores were transformed to range between 0 and 100. PCS and MCS scores were calculated using standard US scoring algorithms [27]. Item allocation to the eight domains identified by the SF-36 can be found in Table 1.

An NM conceptualizes HRQoL as a network of mutually interacting characteristics [28]. NMs consist of two elements: nodes (circles; observed variables) and edges (lines; relations between variables [29]). To obtain a network, we estimated a Gaussian Graphical Model (GGM) [30] for all samples on both the item level and the domain level, a network in which an edge indicates a nonzero partial correlation between two nodes, while controlling for all other nodes in the network. This means that two connected nodes display a level of covariation that cannot be explained by other nodes in the network. To control for spurious connections that may arise due to multiple testing, and for the computational size of the problem, we applied the graphical lasso [31]: a form of lasso regularization [32], which utilizes penalized maximum likelihood estimation. The result is a sparse GGM in which many edge weights are set to zero and thus removed from the network. The network that is formed with a graphical lasso is therefore both interpretable and guarded against overfitting. The graphical lasso uses a tuning parameter to control the sparsity of the network, which we chose by minimizing the Extended Bayesian Information Criterion [EBIC; 33]. This methodology is explained in more detail by Costantini et al. [34]. Because the GGM assumes that the input covariance matrix comes from a population that follows a multivariate Gaussian density, whereas the SF-36 only measures at an ordinal scale, we computed the polychoric correlation matrix to apply the graphical lasso.

We checked for differences in network structures by means of a permutation test developed by van Borkulo et al. [35]. The difference is defined as the deviation in absolute weighted sum scores of the connections [36]. This permutation-based test randomly regrouped participants from the cancer patient sample and the national sample repeatedly (1000 times) and calculated the differences between these subsamples. The resulting distribution under the null hypothesis (both subsamples are equal) is used to test the observed difference of the original subsamples against a significance level of 0.05. Both weighted network structures (taking the edges’ weights into account) and unweighted network structures (only taking the presence of an edge into account) were tested. The latter is tested to investigate whether the basic structure of the samples are similar, the first to investigate whether the strength of individual connections in the networks structures are similar.

To analyze the place and function of items within individual networks, we use the measure of closeness centrality [37]. Edges between nodes are interpreted as paths: the stronger the edge, the stronger the path between relevant nodes, and the easier it is to travel from one node to another [34]. A highly central node is one from which it is possible to easily travel to all other nodes. Such paths may be interpreted as etiological progressions by which individual problems can lead to closely connected problems. In particular, nodes with high closeness centrality have a high ability to predict other nodes, and as such they may correspond to characteristics that have a particularly important function in HRQoL. Adopting the formal interpretation of HRQoL, when an item has a high closeness, it predicts HRQoL well.

All analyses were performed using the R statistical software 3.1.2. GGMs were constructed with the R-package huge version 1.2.6 [38]. Network visualization and the computation of centrality measures were done with the R-package qgraph version 1.3.1 [39].

A total of 2227 participants completed the SF-36. As shown in Table 2, the mean age of the cancer patient sample (N = 485) was 57.27 years with 58 % women. The national sample (N = 1742) had a mean age of 46.71 years with 44 % women. Table 2 also shows the mean scores on the eight domains of the SF-36 for the individual samples.

Figure 2a–c show networks of the cancer patient sample, the national sample and the combined sample, respectively. Edges between nodes within a network correspond to polychoric partial correlations between items, controlling for all other items. The stronger a connection between two nodes, the thicker and more saturated the edge. Positive and negative connections are denoted by green and red edges, respectively [34]. Each node corresponds to a single SF-36 item (as given in Table 3) and is colored according to the domain it is allocated to (as given in Table 1). Item 2 and the diagnostic status variable are not part of any domain and thus are represented as separate. The Fruchterman–Reingold algorithm, which places more strongly connected nodes closer together, is used for node placement in all networks [40].

As seen in Fig. 2a–c the global structure of each network reflects the domains set up by Ware et al. [41]; items that belong to the same domain are closely connected and cluster into predetermined domains.

These results comply with the idea that the covariance between items may result largely from direct interactions between observables, rather than from the common influence of a latent HRQoL variable. For instance, items 5a and 5b are strongly connected, which likely reflects a potential causal relation, because being able to spend less time on work will typically lead one to accomplish less. Another example is the strong connection between items 7 and 8, which is visible within all networks. However, there are also connections that are more likely to reflect bidirectional influences. An example is the relation between items 9c and 9f, where feeling down in the dumps and feeling downhearted and blue influence each other. Finally, some strong connections arise because items formulate necessary conditions for other items (exemplifying potential deterministic causal relations). For instance, there exists a strong correlation between items 3g and 3h. This correlation plausibly arises because walking 1 km requires the ability to walk a few 100 m, such that the latter is a necessary condition for the former.

Figure 3a–c shows networks of the eight domains present in the SF-36 for the cancer patient sample, the national sample and the combined sample. It can be seen that domains whose items are near each other in Fig. 2 are strongly connected. For example, the domains mental health (MH) and vitality (VT) have a strong connection in all networks. Interestingly, there exists a strong connection between bodily pain (BP) and social functioning (SF), while this is not visible in Fig. 2, where item networks are displayed.

We compared the item network structures from both the cancer patient sample and the national sample. We found that these two network structures are dissimilar (p < .001) when comparing weighted network structures, but we did not find dissimilarity when comparing the unweighted network structures (p = .056). Although care must be taken in interpreting null results in hypothesis testing, this suggests that the basic structure of the SF-36 in the cancer patient sample resembles the structure found in the national sample. However, it should be noted that this does not rule out the existence of local differences in the network structure, as statistical power to detect local differences is limited.

The same analysis was performed for the domain networks. We could not reject the null hypothesis that the network structure is invariant over subpopulations, when comparing the unweighted network structures (p = .16) as well as when we compared the unweighted network structures (p = .18). Results indicate that the domain network structure generalizes to different subpopulations quite well.

Figure 4 and Table 4 display the closeness centrality measures for all item networks. When inspecting closeness centrality, we find that the three networks mostly agree on which items are most central. As seen in Table 4, items 4b, 4c, 4d, 9g and 9i were most central for the cancer patient sample, items 4a, 4b, 4c, 4d and 5b were most central for the national sample and items 4a, 4b, 4c, 4d and the diagnostic status variable were most central for the combined sample. Items 4b, 4c and 4d are the items that are among the most central items in all networks. This suggests that in all datasets, the ability to keep participating in work or other activities despite one’s physical health has the largest influence on other characteristics in all networks. The networks align less with respect to the least central items. Items 2, 3d, 3e, 3f, and 7 were least central for the cancer patient sample, items 2, 7, 8, 9b and 11c were least central for the national sample and items 3e, 3g, 3h, 3i and 9b were least central for the combined sample. There were no items that were among the least central items in all networks, but items 2 and 7 were among the least central items in both the cancer patient sample and the national sample. Remarkably, this suggests that one’s perception of one’s health compared to 1 year ago and the amount of bodily pain during the past 4 weeks hardly influence other characteristics in the cancer patient sample network and the national sample network.

Figure 5 and Table 5 display the closeness centrality measures for all domain networks. When inspecting closeness centrality, we find that the three networks disagree on which items are most central. As found in Table 5, the domains general health (GH) and BP are most central in the cancer patient sample, the domains role limitations–physical (RP) and role limitations–emotional (RE) in the national sample, and the domains RP and PF in the combined sample. The cancer patient sample and the national sample do not share any domain that is among the most central domains in the networks. The domain PF is among the most central domains in both the cancer patient sample and the combined sample, and the domain RP is among the most central domains in the national sample and the combined sample. This suggests that physical health and possible limitations as a result of one’s physical health have the largest influence on other domains in the networks.

The same non-alignment is found in the least central domains. Domains SF and BP are considered the least central domains in the cancer patient sample and the national sample, and the domain MH and the diagnostic status variable (DS) were the least central domains in the combined sample. It can be seen that the cancer patient sample and the national sample regard the same domains as least central. This suggests that the interaction between social functioning and bodily pain on the one hand, and the rest of the domains on the other hand, is less strong compared to other interactions in the network. This may mean that they have the least influence on other domains in the network, that they are least sensitive to changes in other domains or that the variance in these domains is largely determined by factors outside of the network structure.