Highways to happiness for autistic adults? Perceived causal relations among clinicians

Marie K Deserno; Denny Borsboom; Sander Begeer; Riet van Bork; Max Hinne; Hilde M Geurts

doi:10.1371/journal.pone.0243298

. 2020 Dec 15;15(12):e0243298. doi: 10.1371/journal.pone.0243298

Highways to happiness for autistic adults? Perceived causal relations among clinicians

Marie K Deserno ^1,^2,^*, Denny Borsboom ², Sander Begeer ³, Riet van Bork ², Max Hinne ², Hilde M Geurts ^1,²

Editor: Geilson Lima Santana⁴

PMCID: PMC7737981 PMID: 33320901

Abstract

The network approach to psychological phenomena advances our understanding of the interrelations between autism and well-being. We use the Perceived Causal Relations methodology in order to (i) identify perceived causal pathways in the well-being system, (ii) validate networks based on self-report data, and (iii) quantify and integrate clinical expertise in autism research. Trained clinicians served as raters (N = 29) completing 374 cause-effects ratings of 34 variables on well-being and symptomatology. A subgroup (N = 16) of raters chose intervention targets in the resulting network which we found to match the respective centrality of nodes. Clinicians’ perception of causal relations was similar to the interrelatedness found in self-reported client data (N = 323). We present a useful tool for translating clinical expertise into quantitative information enabling future research to integrate this in scientific studies.

Introduction

An Autism Spectrum Disorder diagnosis (ASD) is related to reduced levels of well-being [1–3], but the study of risk and protective factors for well-being in autistic individuals is still at an early stage, in particular for adults. After at least a decade of well-being research in autistic adults, we lack sophisticated understanding of the potential causal pathways that channel the heterogeneity in adult outcome of the autistic population. Most research on later outcome and well-being in autistic individuals aspires to the principle of Ockham’s razor, looking for a set of simple basic elements or a latent entity that could explain the emerging phenomenological complexity. In addition, most of these attempts study the interrelatedness of two or three variables instead of the complex system of possible pathways that extend further than just two variables.

Network approach to psychological phenomena

Recent theoretical literature, however, has argued for a more complex approach to human development inspired by dynamical systems theory [4–7]. This theoretical shift has enabled the emergence of the network approach, an alternative psychometric conceptualization in which psychological phenomena are seen as a dynamic set of causally intertwined properties [8, 9]. The starting point in the network approach is to determine the relational structure between symptoms and other factors and to represent this information in a network. Currently, there are at least four approaches to the construction of these networks. First, by examining associations between symptoms in a population [10]; second, by inspecting the dynamic structure of a network over time [11]; third, by utilizing the structure of diagnostic manuals [12, 13]; and fourth, by eliciting judgements on the structure of causal relations between symptoms, either from clinicians [9, 14] or through self-report [15]. In our recent network studies, we have studied the association network of interacting factors for the subjective well-being of autistic individuals [16, 17] based on self-report survey data. However, the inclusion of clinical expertise is largely lacking. Furthermore, due to the explorative character of network analysis as a statistical method, it is unclear whether relations between variables identified in these studies actually reflect causal interactions (rather than, e.g., the effect of unmeasured common causes). In addition, it remains unclear what the direction of these causal pathways is, and to what extent the relevant pathways are also identifiable by other, independent modes of observation. The primary purpose of this study is to quantify multivariate causal beliefs of clinicians specialized in autism care and to map those onto multivariate empirical interrelations found in self-reported data of autistic adults, which will add to the understanding of causal relations between autistic characteristics and well-being.

Perceived causal pathways in the well-being system

The general interest in causal pathways in the well-being system is a crucial issue. Most of the network literature, however, focuses on the first of the above-mentioned methodologies: the analysis of association structures in population data. Although association networks are a good way to develop insight into the association structures among symptoms, to be able to develop interventions and policy based on network information on ASD and well-being, we need to gain more insight into the directionality of the interrelations. For example, if cognitive problems associated with autism are connected to depressed mood in an association network, does that mean that specific cognitive problems associated with ASD cause depressed mood? Alternatively, does depressed mood cause cognitive problems associated with ASD, or are the variables connected in a feedback loop? That is, the field is in dire need for a toolbox that can help us determine which connections in the network represent directed causal effects that arise from reciprocal causation or coupled equilibria, and which associations are due to the effect of unmeasured variables.

Clinical validation of networks based on self-report data

From earlier research we have learned that, when asked how symptoms relate to each other, clinicians report a network of interacting symptoms [14, 18]. This suggests that professionals working in the clinical field may already conceptualize psychological constructs as a set of causal relations between symptoms and other factors. A brief review of the existing network literature shows that this source of information has not yet been integrated in network approaches. It remains unclear, for instance, whether the network structures shown in self-reported data actually resemble those networks that clinicians would report. In other words, when compared to the networks based on self-reported data from earlier studies on factors relevant for well-being in ASD (e.g., insistence on sameness, experiencing reduced contact and struggling with social conventions; [17]), do experienced clinicians report a similar pattern of causal relations between these factors? And beyond that: do clinical professionals, based on their own experience and knowledge, intuitively choose to intervene on those factors that a network analysis would reveal as most influential in the network?

Integration of clinical expertise into empirical network studies

Even though many researchers aim to bridge the gap between scientific studies and clinical experience, they struggle to find a way to integrate the knowledge of experienced clinicians. One prominent reason for this might be a lack of tools to examine clinician experience in a structured way. Usually, this type of investigation would result in qualitative data from face-to-face interviews with clinicians, which is extremely important but difficult to integrate with common analytical tools in quantitative psychological science, which are statistical in nature. Recent studies, for example, have used the Delphi methodology (a structured interview/communication technique) to investigate the array of clinical practices used in the ASD realm [19, 20]. When exploring the causal relations in a network of symptoms and other clinically relevant factors, however, urgent questions remain: How can we represent the qualitative information in a formal system so that we can integrate the knowledge of experienced clinicians into the network framework? Can methods that elicit such expert knowledge be combined with network analysis of survey data to obtain a better picture of the structure of a problem domain?

The current study

The first aim of the current study is to address the questions raised above, by constructing a symptom network on the basis of expert judgments [15] to visualize the relationships among characteristics of ASD and multiple facets of outcome and well-being. The second aim is to combine this information from clinical experts with the empirical networks obtained from statistical analyses of survey data. To this end, we utilize the methodology of Perceived Causal Relations scaling (PCR; [15]), which provides simple yet promising tools to assess perceived causal relations between variables, and combine this methodology with network analyses on self-reported data. In PCR scaling, any type of informant (i.e., rater) can be asked to what extent they attribute a causal relationship to a combination of a specific factor X and specific factor Y. Recent studies implementing this scaling technique have used it to get a self-reported representation of symptom-to-symptom interactions administered to individuals experiencing symptoms related to posttraumatic stress and anxiety [15], repetitive behaviors [14] and posttraumatic stress and eating disorders [21]. With this methodology, not only patients themselves but also knowledgeable experts can serve as raters who provide attributions concerning causal interrelationships between factors of interest [14].

In this exploratory study, we (i) identify clinicians’ perceived causal relations between ASD characteristics and domains of well-being (as presented in [17]) and intervention targets within this causal network, (ii) investigate the resemblance of the clinician’s perception of how factors in the network of ASD and well-being are interrelated and the association network based on the interrelations of these factors found in self-reported data, and (iii) provide an example of how to integrate knowledge of clinicians in empirical studies.

Methods

Participants

Experienced clinicians working with autistic adults will serve as raters of the constructed PCR scale which will enable us to calculate the inter-rater reliability for the causal belief network. Twenty-nine clinicians were included in the current study. These clinicians were selected based on their years of experience with ASD to serve as raters in our rating task, i.e., the PCR scale. Potential raters were contacted through Dutch institutional networks such as the dr. Leo Kannerhuis (a nationwide specialized autism clinic), CASS18+ (the national network for autistic adult healthcare professionals), and institutions that are associated with the Academic Centre Reach-Aut, a collaborative network of autistic individuals, relatives, clinicians, and researchers. Since we distributed the questionnaire through the three leading clinical networks within the autism realm in the Netherlands and targeted specific individuals of which we knew that they are (clinically speaking) the authority in the Netherlands, we know that all clinicians participating in the current study are involved in diagnostic work, consultation, and intervention services. Also, these institutes all work with multidisciplinary teams as is recommended in the Dutch Multidisciplinary guidelines for ASD [22]. We included clinicians who are (1) an officially registered psychologist or psychiatrist, a behavioral scientist or social worker with (2) at least five years of clinical experience with adults in ASD health care. If a clinician decided to participate, they were asked to fill in the online informed consent following a link that we provided in an email. Raters were invited for the study online, and completed the PCR-assessment on their own via the internet at a place of convenience. Eventually, twenty-nine clinicians were recruited from various mental health institutions and universities to serve as raters in the current study. Most of them were clinical psychologists (N = 19), a smaller group indicated to work as a psychiatrist (N = 6) or another profession (N = 4). The majority of the participants were female (N = 21) with an average age of 48.5 (SD = 11, range: 30 to 66 years). The average clinical experience was twenty-three years (SD = 10, range: 7 to 50 years) of clinical work with, on average, 14 years (SD = 10, range: 4 to 50 years) of clinical experience in the autism realm. On average, they reported 22.5 hours of clinical work per week with hours ranging from 3 to 37 per week. The research reported in this manuscript has been approved by the Ethics Committee of the University of Amsterdam (2016-BC-7452).

Measures and procedure

First, following the technique of Frewen et al. [15], perceived causal associations between a set of factors (presented below) were rated by experienced clinicians using a PCR scale. The scale requires a rating for each direct relation for each pair of items, which enables us to deduce perceived causal relationships between the network factors. The relations inferred from the PCR network were analysed and interpreted. Second, the PCR network was mapped and compared to the association network of the exact same variables based on self-report found in Deserno et al. [17]. In that study, we estimated network structures relating autism symptomatology to daily functioning and subjective well-being in 323 adult individuals with clinically identified autism (aged 17 to 70 years).

For the current study, we constructed a PCR scale with the online survey software Qualtrics (Qualtrics, Provo, UT) based on the 34-node-network based on self-reported data from an earlier study ([17]; see Fig 1) with a technique drawn from Frewen et al. [15]. In our earlier study, the resulting network was based on items (and subscales) from three measures relevant for well-being in ASD: the Adult Social Behavior Questionnaire (ASBQ; [23]), the Manchester Short Assessment of Quality of Life (MANSA; [24]), and the Health of the Nation Outcome Scales (HoNOS; [25]).

Fig 1 — This association network depicts the interrelations between ASD characteristics, well-being domains, and aspects of daily functioning as found in self-reported data presented in Deserno et al., 2017. The nodes represent the same variables as depicted in Fig 3, while the edges represent regularized partial correlations between those variables.

The ASBQ has been developed to yield an individual’s self-reported score profile among six ASD domains: reduced empathy (N_items = 7), reduced contact (N_items = 7), reduced interpersonal insight (N_items = 8), violation of social conventions (N_items = 6) and insistence on sameness (N_items = 8). We included all six subscales of the ASBQ in the PCR scale.

The MANSA has been shown useful to obtain accurate quality of life data [24]. The self-report questionnaire consists of 26 items covering 14 subjective well-being domains, such as general life satisfaction, social satisfaction, and satisfaction with personal safety. We included all 14 items of the MANSA in the PCR scale.

The HoNOS is a viable instrument designed to assess daily functioning in different domains. The questionnaire consists of 12 items covering, for example, behavioral problems, social problems and cognitive problems. We included all 12 items of the HoNOS in the PCR scale.

The association network derived from the Deserno et al. [17] study was based on regularized partial correlations, where edges depict unique relationships between sets of variables controlling for all other variables in the network ([26]; see [27] for an accessible overview). To work with the exact same network elements as in the previous study, we constructed the PCR scale based on the 32 items mentioned above plus two additional variables: age and number of co-occurring diagnoses. Raters were asked cause-effect questions in regard to each item from the constructed PCR scale, e.g. “To what extent do you think depressed mood causes cognitive problems associated with ASD?” and, likewise, “To what extent do you think cognitive problems associated with ASD cause depressed mood?”. For any given item pairing, participants rated the perceived causal association with response options from 0 to 10, with 0 and 10 denoting “Not at all” and “Strong cause”, respectively. With this scaling methodology, one can gain insight into how clinical experts themselves perceive the causal organization of the given elements in a network.

Since all possible combinations of the 34 network elements would have resulted in 1122 cause-effect ratings, administering all item combinations to all clinicians proved infeasible. Therefore, we decided to split the association network in three (overlapping) parts with an (almost) equal number of nodes (j₁ = 14, j₂ = 13, j₃ = 13) based on their clustering in the association network to ensure study feasibility. That is, we grouped items optimizing three parameters: clustering (i.e. cutting through as few edges as possible), equal group size and overlapping nodes with high degree centrality. This resulted in three subsets of variables, each covering items from two of the three scales. Hence, we chose reliable ratings, i.e. large rater groups, of a subset of all possible cause-effect ratings above less reliable ratings, i.e. small rater groups, of the full network. This also allowed us to assess inter-rater reliability. Clinicians who agreed to participate were randomly assigned to one of these three parts (N₁ = 10, N₂ = 10, N₃ = 9) and were asked to complete the PCR scale based on their clinical experience. Afterwards, we combined the information given by the 29 different raters to partially reconstruct the network structure insofar as the design allows (see below). We conducted a follow-up assessment in which we asked clinicians who had participated in the first rating task to take a look at the constructed network, based on their averaged ratings, and choose three intervention targets from the complete list of nodes depicted in the network visualization. Note that by limiting the answers to three targets we tried to avoid complete rank-ordered lists of all factors. The number of three answers was arbitrary in itself, but a limited number of intervention target resembles a typically realistic intervention context.

Statistical analysis

(i) Perceived causal pathways in the well-being system

First, in order to explore the network of factors, we constructed a network model based on the information retrieved with the PCR scale from the raters in this study, see Fig 2. The perceived causal relations that the clinicians indicated were recorded and averaged to create an adjacency matrix, where each cell represents the averaged value attributed to the relation between any two factors. We merged the resulting matrices from the three rater groups that rated different (but partly overlapping) parts of the network to create a partial reconstruction of the association network based on self-reported data with the exact same nodes. Each value in the adjacency matrix represents the cause-effect rating from the PCR scale made by the raters, with all unrated factor pairings coded as missing. We constructed the directed network (i.e., perceived causal network) from this merged adjacency matrix using the R-package “qgraph” [26]. For reasons of clarity and comprehensibility, only those relations endorsed by the raters with an average rating of at least 6 (on a scale from 0 to 10) on the PCR scale were included in the visual representation of the network. Manually thresholding the visual representation was necessary since the raters tended to attribute very high values to edges they thought were present. Please note that we did not specify such thresholds for any of the analyses described below. We explored network characteristics, such as degree centrality [28 - the number and strength of in- or out-going connections each factor has. Out-degree refers to the sum of the weights of the edges leaving a node, whereas In-degree reflects the sum of the edge weights of connections arriving at a node. In other words, we investigated what factors (i.e., nodes) are perceived to have a lot of causal influence (i.e., edges) on other factors and what factors are more often attributed as the effect (In-degree) versus cause (Out-degree) in all factor-pairings.

(ii) Clinical validation of networks based on self-reported data

Second, we took three different approaches in comparing the network structures found in the different PCR rater groups to the respective substructure of the association network from Deserno and colleagues [17] shown in Fig 1. In a first step, we focussed on the global structure of these networks, ignoring the quantitative differences in edge weights. In this step, our main question was whether the edges that are present in the respective part of the PCR network are concordant with the association network based on self-reported data. To address this issue, we classified positive and negative edges according to the valence of the edge weight, i.e. the mean rating of that specific edge and calculated the proportion of edges that are concordantly classified as either positive, negative, or absent in both networks—compared to (i) all possible (present and absent) edges in the PCR network and (ii) compared to the edges present (both positive and negative) in the two networks. In a second step, we then focused on whether the edges that are present in both networks are similar in terms of weight. Because the PCR network is directed, while the association network is not, to be able to compare the two adjacency matrices, we averaged the unidirectional perceived causal relations between each two variables in the PCR network, resulting in a symmetric adjacency matrix with only one bidirectional coefficient for each pair of nodes; our justification for this procedure is that more directional effects should result in higher partial correlations as represented in the association network. To enable all readers to replicate the analysis, both adjacency matrices are provided as supplementary material online.

(iii) Integration of clinical expertise into empirical network studies

In a third step, we implemented a Bayesian framework for integrating a priori knowledge (here: the structure of the rated networks) in the estimation of an empirically derived network structure (here: the association network based on self-report data). Using the PCR framework as a scaffolding structure, we constrain the estimated association network based on self-report data (following [29]): those connections for which our raters indicate no evidence are forced to be absent in the estimated network. Simultaneously, the connections that correspond to a perceived causal relation are estimated from the data as usual. The practical implication of this is that our network consists only of the relations that we know a priori to be relevant. At the same time, this has methodological advantages as it reduces the number of free parameters to be estimated, which means fewer observations are required for accurate network estimation results. Because this integration requires a design in which ever edge is assessed by both of the relevant techniques, we could only apply this technique for the three subnetworks that had been both completely rated by experts and assessed empirically (see Methods section). In this constrained estimation approach, first the symmetric PCR adjacency matrices are used to define the probabilities of connections per node pair. Second, a thousand constrained networks are generated at random, using these symmetric PCR scores of each edge as its probability. Third, we use the R-package BDgraph to estimate the association network weights, given the provided structure. Fourth, we average the estimated networks across the generated samples, to come up with the Bayesian model average estimated network.

Results

Most raters answered all questions, with only 10% of all PCR ratings missing. We did not replace these values and used all available data for the network analysis. We looked at the consistency of these ratings across raters: Cronbach’s alpha indicated good inter-rater reliability within each group of raters, 0.85, α₂ = 0.80, α₃ = 0.85.

(i) Perceived causal pathways in the well-being system

Many associations apparent in the association network, shown in Fig 1, remain in the PCR network. Fig 3 depicts the directed network based on the clinician ratings. Several features are notable.

First, the PCR network was highly connected. This means that clinicians indicate that there is a dense set of perceived causal effects involving the relevant variable, supporting the idea that they indeed form a causal network. Compared to previous PCR network studies [14, 15, 21], we found exceptionally few feedback loops. Only reduced social insight (rsi) and violations of social conventions (vsc); reduced empathy (remp) and reduced social contact; the number of comorbid diagnoses (comNR) and comorbid problems (com); depressed mood (dep) and satisfaction with life in general (lif); and psychological well-being and depressed mood (dep) were attributed bidirectional relationships.

Second, among all ASD symptoms included in the network, reduced social insight (rsi) ranked highest on both In- and Out-degree. In other words, clinicians rated reduced social insight more often as the cause of variance in other (central) factors, such as problems with relationships (rel), but also as the underlying cause of variance in (most) other ASD characteristics, i.e., reduced empathy (remp), reduced social contact (rcon), violations of social conventions (vsc) and insistence on sameness (inss). Also, one other ASD symptom ranked among the four variables highest on In- and Out-degree: insistence on sameness was attributed both many incoming and many outgoing connections, with for example a strong negative connection to psychological well-being (psy). From all ASD characteristics, sensory stimulation/motor stereotypies (ssms) was the one with least connections in the network as it was only rated as a cause for problems associated with daily life (adl).

Third, centrality indices (see Fig 4) showed depressed mood (dep) and problems with relationships (rel) as having the highest degree centrality amongst both well-being (brown) and daily functioning variables (rose). In addition, depressed mood ranked, together with psychological well-being, highest on betweenness centrality in the network, i.e., these nodes most often funnelled the shortest path between two other nodes in the network. Also, depressed mood was the strongest predictor for both low psychological well-being and low general life satisfaction (lif), closely followed by reduced social contact (and insistence on sameness for psy) and physical problems (phy; for lif).

Finally, age and sexual well-being (sex) were attributed neither incoming nor outgoing connections. This means that our clinician sample did not consider these variables relevant causes of other variables in the network, and, in case of sexual well-being, a result of any other variable in the network (please note that we did not include any questions asking participants to rate the effect of some variable in the network on age, because this would not result in meaningful questions, e.g. “How much do you think depressed mood causes age?”).

Half of the clinician sample (N = 16) responded to our follow-up question, inquiring about where they would intervene in the provided network (Fig 3) if the goal would be to improve the general well-being of an autistic individual. For this follow-up question, we did not collect personal information. The question resulted in a broad spectrum of intervention choices: 21 out of 34 nodes were at least once chosen to be a target of intervention. The top four choices, however, were depressed mood (dep; 7 votes), hyperactive or aggressive behavior (agr; 5 votes) and reduced social insight (rsi; 4 votes) and problems with drugs (drug; 4 votes).

(ii) Clinical validation of networks based on self-reported data

In this section, we compare the clinician network and association network based on two classifications that characterize the structure of the network: the presence/absence of edges and their valence (positive/negative).

To be able to zoom in on different parts of the networks, we depicted the relationship between the values of the PCR network and the association network in Fig 5, which presents a scatterplot of the edge weight values in the clinician and the association network and visualizes information on both the concordance in structure of the networks (whether an edge is present or absent in both networks) and the alignment of the relative strengths of edge weights in the networks. The values that form a line parallel to the x-axis represent edges that are 0 (absence of an edge) in the association network. The reason that there are no values parallel to the y-axis (absence of an edge in the clinician network) is that the averaged ratings never resulted in a value of exactly 0. The values in the lower left section of the scatterplot represent edges that are negative in both graphs (e.g., agreement in both networks that psychological well-being has a negative relation with depressed mood), while the values in the upper right section of the scatterplot represent edges that are positive in both graphs (e.g., agreement in both networks that hyperactive or aggressive behaviour has a positive relation with drug problems). The two values in the lower right section are edges that are positive in the clinician network and negative in the association network, while the value in the upper left section represents an edge that is negative in the clinician network and positive in the association network (i.e., the findings in the two networks are opposed to each other).

Fig 5 — The values on the line parallel to the x-axis represent absent edges (value of 0) in the association network. The values in the lower left section represent edges that are negative in both graphs, while those values in the upper right section represent edges that are positive in both graphs.

One possible metric to investigate the similarity of the networks’ structure is to simply look at the proportion of edges that are concordantly classified as either positive, negative or absent in both networks. Since there we no edges in the clinician network with a value of exactly 0, we decided to look at the concordant classification of present, both positive and negative, edges in the two networks. Making this comparison, 96% of all edges present in the clinician network are matching in their classification as positive or negative when compared to the association network.

When comparing the weighted edges that are present in both networks, a first clear difference that can be inferred from the adjacency matrices is that the weights that result from the average ratings of the clinicians are higher (both negative and positive weights) than the regularized partial correlation values. This difference in magnitude of the weights should not be interpreted in substantive terms, as it follows simply from the different scale of the weights: weights in the association network reflect partial correlations scaled between -1 and 1, whereas the weights in the adjacency matrix of the clinicians result from averaging the ratings of clinicians on a scale from 0–10. For this reason, to compare the association network to the PCR network we consider only the relative magnitude of the weights. That is, are the edges that are relatively strong in one network also relatively strong in the other network?

Because not all edges that are present in the association network have been rated by the clinicians, we calculated the correlation between the weights in both networks only for those edges that had actually been rated by the clinicians. This results in a correlation of r = 0.43. This correlation is likely to be a lower bound estimate for the correspondence between networks, because the graphical lasso indiscriminately sets edges to zero when they contribute insufficiently to model fit (this produces the horizontal pattern of points in Fig 5), while the PCR network provides continuous variation across all edge weights. This leads to restriction of range in the graphical lasso estimates, which is well known to attenuate the correlation coefficient. Therefore, we also looked at the relationship between the edges that are present in both networks and as such cannot be affected by restriction of range (i.e., only those values that are located away from the horizontal axis corresponding to the value zero in Fig 5). This correlation coefficient equals r = 0.73, mainly driven by the fact that most positive edges in the association network were also attributed positive edge weights by the clinicians and most negative edges in the association network were attributed negative weights. When zooming in on the correlation of only the negative edges present in both networks (depicted in the lower left section of Fig 5) or only the positive edges present in both networks (depicted in the upper right section of Fig 5), the correlation coefficient equals 0.57 or 0.62, respectively, indicating moderate correspondence. This suggests that the PCR network and the association network align strongly in terms of the signs of relations between variables, moderately in terms of the magnitude of these relations, and feature a weak-to-moderate correspondence when the pattern of structural zeroes induced by the graphical lasso is not accommodated for, see Table 1.

Table 1. Contingency table for concordant and disconcordant positive (1), negative (-1) and absent (0) links in the PCR and the empirical network.

Empirical
PCR	-1	1
-1	14	3
0	52	107
1	2	66

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We could not include all possible links in the PCR scale, resulting in induced zeros in the adjacency matrix (i.e., absent edges, by design, in the PCR network). We did not include those absent edges in the contingency table nor the calculation of the reported metrics.

(iii) Integration of clinical expertise into empirical network studies

To integrate the clinician and the association network, we applied the constrained estimation approach described in Hinne et al. [29], i.e. the estimation of the ASD association network constrained by the information taken from the clinician network. Because this integration requires a design in which ever edge is assessed by both of the relevant techniques, we could only apply this technique for the three subnetworks that had been both completely rated by experts and assessed empirically (see Methods section). In this constrained estimation approach, first the symmetric PCR adjacency matrices are used to define the probabilities of connections per node pair. Second, a thousand constrained networks are generated at random, using these symmetric PCR scores of each edge as its probability. Third, we use the R-package BDgraph to estimate the association network weights, given the provided structure. Fourth, we average the estimated networks across the generated samples, to come up with the Bayesian model average estimated network.

Fig 6 shows the resulting networks, next to the networks as estimated by the graphical lasso for comparison. Qualitatively, these figures confirm that the perceived causal relation structure overlaps greatly with the networks obtained from questionnaires. Only a handful connections are absent in the constrained approach and present in the lasso estimates or vice versa. The connections with a difference larger than 0.1 are:

Subgraph 1

from depressed mood to self-harming behavior
from problems with delusions to self-harming behavior

Subgraph 2

from psychological well-being to insistence on sameness
from having a friend to reduced contact
from visiting friends to reduced contact
from sexual well-being to social satisfaction

Subgraph 3

from satisfaction with not working to relational problems
from general satisfaction with life to depressed mood
from psychological well-being to depressed mood
from satisfaction with not working to problems with developing skills
from problems with living situation to satisfaction about living situation

Interestingly, for the latter two subnetworks, those connections that are excluded by the PCR-constrained estimate, but are present in the graphical lasso estimate, correspond mostly with negative partial correlations.

Discussion

Main findings

The current network study is the first to compare how clinicians think that ASD symptoms and wellbeing are interrelated with how they are interrelated in self-report data based on self-reports by autistic individuals. Results suggest a moderate to strong alignment of the networks arrived at through both approaches, suggesting that clinician networks and association networks may, at least in part, point to the same underlying structure of potentially causal relations. The moderate convergence of these methods also suggests that promising insights are readily available through a synergy between PCR networks and association networks, which could capitalize of the strengths of both. This indicates that the integration of these techniques is a promising methodology that should be further studied, and we have provided the first workable solution to this challenge through the Bayesian constrained estimation approach. Finally, the current study provides the first validation study of widespread network estimation procedures (i.e., regularized network estimation; [27]) to use independent sources of data, by comparing it to PCR methodology to assess expert ratings of perceived causal connections. The alignment between the association network and the PCR methodology supports the validity of both methods, although research investigating sources of divergence is naturally called for as well.

Limitations

When interpreting the similarities and differences of these networks, several limitations deserve mention. First, splitting up the network in three parts for reasons of feasibility resulted in leaving out many pairings of factors for the PCR scale. This means that, as mentioned in the Results section, not all connections present in the association network have been rated by the clinicians. When setting up the study, we chose large rater groups above a complete rating of the association network, which safeguards reliability of the estimated network structures, but limits the breadth of the investigation. Note that for rater- and structured interview studies a sample size of 29 raters is considered large [30]. Future studies, however, might focus on the latter if they aim to advance the thorough comparison of these two types of networks, or may attempt to develop optimal ways of distributing raters across parts of the network to optimize reliability and breadth jointly.

Second, it is important to note that the centrality of nodes in the association network indicates the importance of a node for the given network structure within the autistic population as it is mainly based on self-reported data of people with an ASD diagnosis. This means that, for example, suffering from depressed mood, which is highly central, impacts the state of all other factors in the network for this specific population (assuming that the variables in question indeed form a causal system with symmetric effects). It does not reveal information about whether this is a distinct feature of the network with respect to other populations. For example, when researching a network of factors in the general population and we look at what might be a central predictor for being a successful basketball player, the network is likely to reveal that someone’s height plays an important, i.e., central, role. When looking at the same network of predictors in a sample of professional basketball players, their height will probably not be as central in this population-specific network as they do not vary much in terms of height. In the same vein, the PCR network might differ from the association network in terms of centrality of certain nodes as clinicians might have rated all connections aiming to represent what nodes are important for the autistic population compared to other clinical populations or typically developing people.

Third, in this study we were able to reach out to highly experienced experts in the autism field: the knowledge that we combined into the PCR network was based on about 14 years, on average, of clinical work with people with an ASD diagnosis. This is a very specific sample of experts, of course, which limits the generalizability of the ratings to, for example, other mental health professionals or general practitioners [31]. Also, although we know that the majority works for clinical institutes that have a tradition for over 40 years in specialized autism teams, we did not specifically ask for more detailed information about their background and type of experience. This leaves us unable to assess whether the subgroup asked to choose intervention targets has very specific or, instead, a diverse range of characteristics. Another important factor regarding the generalizability of our results concerns the choice of what factors to include in the PCR rating task. In the current study, this choice was a priori limited by the available data that our association network was based on, i.e. the questionnaires that were implemented in the treatment monitoring systems of a Dutch autism clinic [17]. Relatedly, the association network in this study was based on the limited available data from one specific mental health clinic, resulting in an inability to verify exact IQ scores and lack of generalizability to those with intellectual disabilities.

Relation to the literature

In this study, the PCR network revealed the causal model ASD clinicians adhere to when they think about the interrelatedness of ASD symptoms. Results suggest that, in the first place, reduced social insight is seen as the underlying cause of the other ASD symptoms in the network. Other than the influence of reduced social insight, the ASD symptoms are not attributed any strong incoming connections. Rather, they appear to be seen as exogenous variables, which cause individual differences in well-being and daily functioning, but are not themselves caused by the other variables in the system. This would be consistent with the plausible idea that ASD symptoms arise from sources external to the factors in the current networks, e.g., from problems associated with brain development. For example, early brain parameters are not assessed in the current study, but without any doubt relevant to atypical development (see [32] for an elaborate review of this line of thought). In addition, two pairs of ASD symptoms are connected by strong feedback loops (reduced social insight with violations of social conventions, and reduced empathy with reduced social contact) suggesting the plausible hypothesis that these problems mutually reinforce each other. A strong impact on domains of well-being and daily functioning, on the other hand, is also attributed to the ASD symptom insistence on sameness, which has perceived causal connections to e.g., problems with daily functioning and problems with relationships.

Second, the clinicians’ choices regarding the factors that they would make their target of intervention (depression, reduced social insight, and aggressive behavior) were related to the respective centrality of these nodes in the PCR network. The factor that ranked highest among all choices was also the most central factor in the PCR network: depressed mood. This is in line with literature demonstrating that clinicians causal reasoning when dealing with diagnostic information concerning mental disorders is related to the causal model they adhere to [33–35].

Third, we found that the way this clinicians sample perceive cause-effect relations between ASD symptoms, well-being, and domains of daily functioning is fairly similar to the interrelatedness of these factors found in self-reported data. All links that are present in both the association network from Deserno et al. [17] and the current PCR network, are attributed similarly weighted cause-effect ratings by the clinicians as their edge weights found in the self-reported data. At the same time, this suggests that clinicians are aware of the specific impact certain variables in the well-being network have on each other, and that the association network may pick up the relevant relations as well. This finding is concordant with earlier research, which has suggested that clinicians’ personal cause-effect models affect their diagnoses [18] as well as their judgement of the effectiveness of a specific intervention [36–38]. The congruence of these cause-effect ratings with the relationships found in self-reported data suggests that there are no major gaps between the two concerning those relationships that are present in both networks. It is important to note, however, that not all possible edges in the well-being network were rated by the clinicians, so we are unable to assess to what extent this result generalizes to parts of the network that have not been rated.

Outlook

In sum, we have presented a useful way of translating clinical expertise in the ASD realm into quantitative information and hereby illustrate a promising way to integrate clinicians’ knowledge into scientific studies. Future studies could use these tools to quantify different types of knowledge. For example, as many voices have been campaigning for more participatory research in (not only) the ASD realm, the PCR methodology could be used to build new models and generate hypotheses in cooperation with groups of autistic people or any other knowledgeable informant. Advances in this methodology could even result in a tool worth implementing in treatment and diagnosis. The schematic representation of perceived cause-effect models might benefit both clinician and client in any mental health setting (see also [39]). In addition, we have provided the first validation of psychological network estimation procedures that have been energizing different clinical fields in psychological science. The combination of association networks and PCR ratings offers a promising framework to assess the validity of network structures found in self-reported data. However, in order to structurally compare these types of networks, it is important to develop advanced statistical techniques in future research. Here, we illustrate what important insights are to be gained into the interrelatedness of ASD and well-being by using the PCR methodology alongside self-reported data. We are convinced that the complementary use of quantified clinical expertise and self-reported data offers novel opportunities to study the workings of any multi-causal complexity in clinical psychology.

Supporting information

S1 Table. List of abbreviations of nodes in the networks.

(DOCX)

Click here for additional data file.^{(13KB, docx)}

S1 Data

(CSV)

Click here for additional data file.^{(21.2KB, csv)}

S2 Data

(CSV)

Click here for additional data file.^{(5.8KB, csv)}

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

DB is supported by ERC Consolidator Grant no. 647209. HMG is supported by the NWO VICI Grant no. 453-16-006. This research project is supported by ZonMW Grant no. 70-73400-98-002.

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PLoS One. doi: 10.1371/journal.pone.0243298.r001

Decision Letter 0

Geilson Lima Santana

7 Aug 2020

PONE-D-20-15702

Highways to happiness for ASD adults? Perceived causal relations among clinicians

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Reviewer #1: Review PONE-D-20-15702

### GENERAL COMMENTS

In general, this is a very interesting manuscript that has used a network methodology to shed a light on a new methodology (PCR) to catch clinical aspects regarding ASD.

Nevertheless, to achieve the best shape, the manuscript needs some modifications.

The authors tend to use several personal choices to justify some decisions (without a proper rationale to justify them).

Two of the most problematic topics of the article are:

a) the sample size (n=29 completed the questionnaire and n=16 were enrolled in the follow-up),

and b) is the use of the term "causal". If by one side, the authors mention correctly "association network", by the other, they tend to classify associative relationships as causal ones. The authors should avoid using “causal” when data is correlational.

The authors should have more care regarding the organization of the text. For example: the tables are out of order, the methodology and discussion presents redundant parts, and more than once, the reader needs to read again a part of the text to understand the intentions of the authors.

Rather than using the term "empirical data", it would be more appropriate to rename this sort of information as "subjective data" (gathered directly from patients) in opposition to "objective data" (gathered from clinicians regarding their patients). Because, as a matter of fact, this study compares personal observation of patients with observations from clinicians. Since "empirical data are facts that are obtained by observation or experiment", both data used in the research could be considered empirical. The definition used by the authors does not consider as empirical the second cluster of data. Nevertheless, this assumption is not correct.

In a more general comment, to improve the comprehensibility of the text, it would be very interesting if the text was built having blocks of information in mind. Them, pairing each part of the text with the next section.

Example: The concepts and gaps mentioned in the introduction should be paired with the aims of the study. Then, these previous parts being paired with the corresponding part on the methods used for answering each of these questions (in a step-by-step fashion). Followed by the results you have for each of those questions. And, finally, the discussion of these results (still pairing them with the previous blocks), and the presentation of a conclusion

### INTRODUCTION

• LINES 65 to 68 – I suggest the authors place the primary purpose of the study at the end of the introductory section. The same could be said regarding information regarding LINES 90 to 93.

• LINES 85 to 93 – The authors should think about dividing the sentence in two at least. Since it is too long, it is difficult to follow, and the reader has to read again to catch all the information it provides.

• LINES 94, 106, and 119 – The authors should reconsider the use of these numbered steps ("First", "Second", "Third"). It is unnecessary. Moreover, the paragraphs they begin with were the continuity of the literature review on the field brought by the introductory section. So, it doesn't make sense the use of them…

• LINES 108 and 109 – The authors make an imperative affirmation. Nevertheless, it is a supposition. So that they should use a conditional verbal form instead of such an illation.

— "This suggests that professionals working in the clinical field already WOULD/COULD/MAY conceptualize psychological constructs as a set of causal relations between symptoms and other factors"

• LINE 110 – It is not adequate mention "in our view". If the authors made a brief review and from that, they concluded that the sort of information is underused, so mention that. Avoid this sort of postulation. E.g.: "A BRIEF SEARCH IN PUBMED DEMONSTRATES THAT THERE IS LACK OF STUDIES…"

• In general, it is interesting the use of questions to incite the readers toward some aspects of the literature gap. Nevertheless, some of them could be placed as affirmations (and their respective references). The overuse of the questions may give the impression that the authors did not perform a good literature review (what doesn't seem to be the case). But to prevent this imprecise impression, use the questions only for the most important points raised in the text (and only those the authors intend to answer with the present manuscript).

In LINE 133-134, for example, the authors mention: "The aim of the current study is to take the first steps towards addressing these questions…". The problem is: there were so many questions… which one of them do they intend to answer? All of them?

• LINE 125 – Mention, in an apposition, what is Delphi Methodology (e.g.: "a structured communication technique")

• LINE 136 to 137: Divide the aims of the study. The too-long sentence could be better explained as: "The aims are: a)……… , b)………… , c) ………" . This separation will make the text easier for readers.

• The PCR methodology should be explained before the presentation of the aims/objectives (that should be the very last points of the introductory section).

• LINE 147 – I suggest changing the term "client" for "patient"…

• LINE 150 – Suppress "exploratory".

• LINE 151 – Although the proposal of look into Deserno et al. 2017 is interesting, that proposition would be more suitable for the reader if the article would be presented in a medium with hypertextual tools (which is not the case of PLOS ONE). So, explain very briefly, in the introduction, before presenting the aims, the domains of well-being as presented in Deserno et al., 2017.

• LINES 155 – 156 – Why do the authors would like to explicitly mention the PCR as an objective of the paper, deviating from the core results? ("…provide an example of how to use the PCR toolbox to integrate the knowledge of clinicians in empirical studies"). Is it necessary? Wouldn't this interesting work itself be a proper example?

• LINES 156 – 158 – More suitable for the "Methods section".

• LINES 159 – 167 – This paragraph would be unnecessary if the introductory section would be properly constructed. This last paragraph should be dedicated to the aims and objectives (that — in this article — were separated through the introduction. Moreover, sentences such as (LINES 164 – 165): "…the exact same variables found in Deserno et al. (2017)" should be avoided because the reader should have all the essential information for understanding this article without the necessity of reading additional articles. The last sentence should be placed in Methods.

### METHODS

• In general, this section is prolix, presents redundant parts, and is not straightforward. It needs to be reformulated to provide to the reader a better understanding of the step-by-step of what was done.

• Regarding the PCR network, remember always use "'perceived causal' effect" instead of "causal effect", "causal network", "causal relation" etc (ex.; line 270, 272) since one perception does not indicate a real causal effect.

• Concerning the sample, how many clinicians were invited to participate, and how was the acceptance rate among all clinicians invited? How the authors calculated (and eventually decided) that the number of raters was enough (n=29)? How do the authors think about the number of responders in the follow-up phase?

• It is not clear how the selection of the institutions and the raters was done. Do the services investigated were the only facilities treating ASD in the region? Provide a glimpse both the selection of the sample. The simple mention that the authors "knew all the clinicians" is not descriptive enough and does not allow replications. Provide objective criteria.

• Why the authors have included raters with different backgrounds (psychologists, psychiatrists, behavioral scientists, and social workers)? Since their formation is quite different from each other some bias on the appreciation of causal relations could certainly occur. Moreover, the PCR intends to access causal relations based on their clinical expertise. Even though the patients are the same, the clinical perception of a professional is biased by people's academic background.

• In the same direction, mention that because they work in such facilities, "we know that all clinicians participating in the current study are involved in diagnostic work, consultation, and intervention services" is not informative enough. Besides that, how the authors guarantee that some of the respondents do not work only in the administration or coordination of such settings rather than as clinicians.

• The authors should avoid expressions such as: "[they] consider 'advanced experience with ASD'". Such subjective evaluation weakens the methodology of the study. For example, mention only that "we knew that they are [specialists]" is too fragile. Objective criteria would be preferable.

• What is CASS18+? I suppose it is an institution/facility for the treatment of ASD. Explain previously used abbreviations.

• Provide a table with the demographic characteristics of the group.

• Provide to the reader a brief explanation of Deserno et al., 2017 and also Frewen et al., 2012 study. Despite hypertextual material is interesting, this sort of tool is not suitable for PLOS ONE and burdens the reader.

• LINES 204 to 210: That is "Statistical Analysis" rather than "Measure and Procedure".

• FIGURE 1 and 2: The quality of the images does not allow us to read the texts inside the nodes or in the figure legends. Figure 2 needs a legend.

• LINES 243 – 247: The authors mention that: "Therefore, we decided to split the EMPIRICAL network in three (overlapping) parts with an (almost) equal number of nodes (j1= 14, j2=13, j3=13) based on their clustering in the EMPIRICAL network to ensure study feasibility."

Is that correct?

• LINES 243 – 252: This was the less comprehensible part of the methodology. Reformulate it to allow a better understanding. It would help a lot providing a diagram/draw/scheme with the step-by-step of the methodology.

• LINES 250 – 252: "Large rater groups"? The author assessed only 29 raters… It is not clear what was done.

• The low number of raters is a serious question. And, when allocated to one of the three different groups, the number is even smaller (n=9, 10, and 10).

• LINE 259 – Why the authors have asked the raters to choose THREE targets? Based on what rationale?

• LINE 276 – Why the authors have used a threshold of 6? Based on what rationale?

• It also is not clear why, having in mind that the empirical network reflects partial correlations scaled between -1 and 1, why the authors have used clinicians' ratings from 0 to 10. Why not using a Likert scale, for example? Moreover, comparing two different networks with two different methodologies would not be problematic?

• I wonder if the authors could have further commentaries on the following sentence from LINES 276 and 277:

—"Manually thresholding the visual representation was necessary since the raters tended to attribute very high values to edges, they thought were present."

Do the authors did not expect that ceiling effect (since they search for "perceived causal effects", those how seem to the raters more intense would be valued)? Do the way the questions were asked did not favor that behavior?

Moreover, in LINE 278, the authors mention that "did not specify such thresholds for any of the analyses". Nevertheless, they mention that (LINE 275-275) "only those causal relations endorsed by the raters with an average rating of at least 6 (on a scale from 0 to 10) on the PCR were included in the visual representation of the network". It seems that one sentence contradicts the other.

### RESULTS

• The results section is difficult to follow and, sometimes, no distinguished from what should be in the discussion. The sentences are very long, and the excessive number of examples and comparisons with previous studies (that should be done in Discussion rather than in Results — E.g. LINE 339 – 343) makes it difficult for the reader to retain the main findings among the profusion of information.

The text would benefit a lot if the authors would be more direct in reporting the results, commenting them only at the Discussion. Focus on the main findings. Although this sort of methodology provides uncountable information, is detrimental to the text if the authors try to mention everything they find.

• The authors do not present numerical results in this section. This data is important and not presented only as supplementary material (LINE 359).

• All tables and figures must be close to their title, footnotes, and all material referent to them. Is difficult to follow the results because the figures do not present this information. Moreover, the Figures do not have titles, but long explanations of what they represent. That fact indicates clearly that the figures are not self-explanatory as they supposed to be (at least in partly).

• The quality of the images is poor making it impossible to read them.

• LINE 337 – The authors should provide the list of abbreviations in the text and not only in the supplementary material.

• The comprehension of the Tables and Figures should be independent of the text (and vice-versa). They have to contain all the necessary information for their comprehension (e.g. LINE 374 — the table title contains references to the text).

• There is no reference in the text regarding where Table 1 should be placed. It was put in the text but not mentioned.

• LINE 377 – 384: It is not clear why the authors decided to investigate the interventions the raters would do. That's not one of the aims of the study and with such a small number of raters, the results are not meaningful.

• LINE 387 – 393 and 458 – 469: This paragraph is not part of the Results section. It describes the methodology.

• LINE 431: "…between -1 AND 1…"

• Where is Figure 4 that is mentioned in the text?

• LINE 496 – 499: This paragraph is not part of the Results section and should be placed in the Discussion section.

### DISCUSSION

• In general, the Discussion is easier to read than the rest of the text, although repetitive.

Nevertheless, the authors tend to stretch the line in some conclusions to explain some findings. For example, I did not understand the sentence in LINE 526-528: "This would be consistent with the plausible idea that ASD symptoms arise from sources external to the current networks (e.g., from problems associated with brain development)."

How's that, "external to the current network"? Are not all the ASD symptoms part of a neurodevelopmental problem?

Moreover, having assessed only about 30 clinicians from a limited number of facilities is too forced, and — why not say — presumptuous, mention that "we found that the way clinicians perceive cause-effect relations between ASD symptoms".

• Again, in Discussion, the problem of "causal" inferences persists.

• I suggest the following articles in order to improve the quality of the text:

A. Docherty M and Smith R. The case for structuring the discussion of scientific papers. BMJ 1999;318;1224-1225;

B. Horton R. The hidden research paper. ‭JAMA,‭ June‭ ‬5,‭ ‬2002‭—‬Vol‭ ‬287,‭ ‬No.‭ ‬21‬‬.‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

• LINE 523: Wouldn't be "…other than THE influence…" rather than "…other than THAT influence…"?

• LINE 562: I do not think is fair the authors mention that they have used "large rater groups". The sample size is quite small and its subdivision smaller. This should be mentioned as a limitation.

• LINE 568: It is interesting the authors mention that "it is important to note that the centrality of nodes in the empirical network indicates the importance of a node…", but they relegate centrality measures to supplementary material.

• LINE 572 – 573: This is one of the most important sentences in the manuscript that deserves more attention from the authors:

— "…assuming that the variables in question indeed form a causal system with symmetric effects…".

It is important to mention that, based on an assumption that variables investigated to form a causal system according to the current literature, the networks were built. So, it is necessary to caution regarding the conclusions of the study.

• LINE 608: "schematic representation of PERCEPTION OF cause-effect " rather than "schematic representation of cause-effect "

**********

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Reviewer #1: No

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PLoS One. 2020 Dec 15;15(12):e0243298. doi: 10.1371/journal.pone.0243298.r002

Author response to Decision Letter 0

16 Oct 2020

Reviewer #1: Review PONE-D-20-15702

### GENERAL COMMENTS

In general, this is a very interesting manuscript that has used a network methodology to shed a light on a new methodology (PCR) to catch clinical aspects regarding ASD. Nevertheless, to achieve the best shape, the manuscript needs some modifications.

We would like to thank the reviewer for appreciating this manuscript and for suggesting modifications to achieve its best shape. We respond to all points being raised below and have addressed all helpful comments in the revision. We are grateful for the time investment of the reviewer and are convinced that the opportunity to revise the manuscript based on the reviewer’s suggestions has significantly improved our submission.

1. The authors tend to use several personal choices to justify some decisions (without a proper rationale to justify them). Two of the most problematic topics of the article are:

a) the sample size (n=29 completed the questionnaire and n=16 were enrolled in the follow-up),

We respond to this comment below, comment no. 23, where the reviewer explained their concern more extensively. In short, for a rater study, based on the literature of structured interviews, the current study‘s sample size can be considered large (de Villiers et al., 2005). While we are of course aware that this is considered small for empirical studies based on survey data. We have now clarified this with the addition of lines 570 -573.

1. and b) is the use of the term "causal". If by one side, the authors mention correctly "association network", by the other, they tend to classify associative relationships as causal ones. The authors should avoid using “causal” when data is correlational.

We agree with the reviewer that careful use of the term ‘causal‘ is warranted. We have therefore revisited every single use of the word throughout the text and have either removed the word to avoid confusion or added the word “perceived” to stress that the discussed association is the results of a rating technique focused on perceived causality (the Perceived Causal Relation method).

2. The authors should have more care regarding the organization of the text. For example: the tables are out of order, the methodology and discussion presents redundant parts, and more than once, the reader needs to read again a part of the text to understand the intentions of the authors.

We thank the reviewer for spotting these inconsistencies and have carefully re-organized the text and tables to make sure there are no redundant parts anymore and to generally improve its comprehensibility.

3. Rather than using the term "empirical data", it would be more appropriate to rename this sort of information as "subjective data" (gathered directly from patients) in opposition to "objective data" (gathered from clinicians regarding their patients). Because, as a matter of fact, this study compares personal observation of patients with observations from clinicians. Since "empirical data are facts that are obtained by observation or experiment", both data used in the research could be considered empirical. The definition used by the authors does not consider as empirical the second cluster of data. Nevertheless, this assumption is not correct.

We thank the reviewer for this suggestion and have given it extensive thought. In this context, the notion of subjective vs. objective may come with problematic implications: the clinicians’ data (now labeled as such, or as raters data) is also subjective in nature, as it is based on individual perception. At the same time, we agree that a pool of experienced clinicians as information source will be more objective than self-reported data as their judgments are based on different cases and years of experiences with autistic individuals. Based on this line of thought, we have, however, decided to refrain from using the ‘subjective‘ vs. ‘objective‘ phrasing but instead changed the ‘empirical‘ label throughout the data description into ‘self-reported data‘ and used the term ‘association network‘ instead of ‘empirical network‘. We hope to have accommodated the reviewers concern with these changes.

5. In a more general comment, to improve the comprehensibility of the text, it would be very interesting if the text was built having blocks of information in mind. Them, pairing each part of the text with the next section.

We would like to thank the reviewer for this excellent suggestion to improve the comprehensibility of the text. We were trying to structure the introduction with first/second/third before but are much happier with this content-related step-by-step fashion. We chose the following headlines for the three aims of the current study (i) Perceived causal pathways in the well-being system, (ii) Clinical validation of networks based on self-report data, and (iii) Integration of clinical expertise into empirical network studies. We have implemented this structure throughout the whole manuscript, i.e. the introduction, the statistical analysis part of the method section, and the results. For the discussion section we decided to stick to the structure of integrating the reported findings in the broader context of the theoretical background. We have rewritten this section to make it more concise.

### INTRODUCTION

6. LINES 65 to 68 – I suggest the authors place the primary purpose of the study at the end of the introductory section. The same could be said regarding information regarding LINES 90 to 93.

We thank the reviewer for this suggestion and have moved lines 65-68 and lines 90-93 accordingly.

7. LINES 85 to 93 – The authors should think about dividing the sentence in two at least. Since it is too long, it is difficult to follow, and the reader has to read again to catch all the information it provides.

We thank the reviewer for this suggestion and have divided the sentence into two parts. We hope this improves its clarity: “Due to the explorative character of network analysis, it is unclear whether relations between variables identified in these studies actually reflect causal interactions (rather than, e.g., the effect of unmeasured common causes). In addition, it remains unclear what the direction of these causal pathways is, and to what extent the relevant pathways are also identifiable by other, independent modes of observation.”

8. LINES 94, 106, and 119 – The authors should reconsider the use of these numbered steps ("First", "Second", "Third"). It is unnecessary. Moreover, the paragraphs they begin with were the continuity of the literature review on the field brought by the introductory section. So, it doesn't make sense the use of them…

We thank the reviewer for pointing this out and have changed this to the (previously suggested) subheadings listed above, see our response to comment no. 5.

9. LINES 108 and 109 – The authors make an imperative affirmation. Nevertheless, it is a supposition. So that they should use a conditional verbal form instead of such an illation.

— "This suggests that professionals working in the clinical field already WOULD/COULD/MAY conceptualize psychological constructs as a set of causal relations between symptoms and other factors"

We thank the reviewer for this suggestion and have changed this into a conditional verbal form: “This suggests that professionals working in the clinical field may already conceptualize psychological constructs as a set of causal relations between symptoms and other factors.”

10. LINE 110 – It is not adequate mention "in our view". If the authors made a brief review and from that, they concluded that the sort of information is underused, so mention that. Avoid this sort of postulation. E.g.: "A BRIEF SEARCH IN PUBMED DEMONSTRATES THAT THERE IS LACK OF STUDIES…"

We thank the reviewer for this suggestion and have changed our wording accordingly: ‘A brief review of the existing network literature shows that this source of information has not yet been integrated in network approaches. It remains unclear, for instance, whether the network structures shown in self-reported data actually resemble those networks that clinicians would report.‘ (line 112-114)

11. In general, it is interesting the use of questions to incite the readers toward some aspects of the literature gap. Nevertheless, some of them could be placed as affirmations (and their respective references). The overuse of the questions may give the impression that the authors did not perform a good literature review (what doesn't seem to be the case). But to prevent this imprecise impression, use the questions only for the most important points raised in the text (and only those the authors intend to answer with the present manuscript).

We thank the reviewer for this comment and we agree that the overuse of questions can give the wrong impression. We have, therefore, rephrased some of the questions in the introduction into affirmations, see e.g. line 103-105: ' That is, the field is in dire need for a toolbox that can help us determine which connections in the network represent directed causal effects that arise from reciprocal causation or coupled equilibria, and which associations are due to the effect of unmeasured variables.' In (now) lines 129-134 however, we raise two very specific questions that we intend to answer with the current study: a) how to meaningfully represent clinical expert knowledge in network studies and b) how to combine existing methods assessing such expert knowledge with networks of self-reported data. Directly after raising these two questions, we provide a detailed explanation of how we plan to answer these questions by the current study.

12. LINE 125 – Mention, in an apposition, what is Delphi Methodology (e.g.: "a structured communication technique")

We thank the reviewer for this suggestion and have added this information: “Recent studies, for example, have used the Delphi methodology (a structured interview/communication technique) to investigate the array of clinical practices used in the ASD realm (Wainer et al., 2017; Kerns et al., 2018).” (line 129)

13. LINE 136 to 137: Divide the aims of the study. The too-long sentence could be better explained as: "The aims are: a)……… , b)………… , c) ………" . This separation will make the text easier for readers.

We thank the reviewer for this suggestion and have divided the too-long sentence presenting the current study‘s aims to make it easier to read: “The first aim of the current study is to take the first steps towards addressing these questions, by constructing a symptom network on the basis of expert judgments (Frewen et al., 2012) to visualize the relationships among characteristics of ASD and multiple facets of outcome and well-being. The second aim is to combine this information with the information obtained from statistical analyses of survey data.”

14. LINE 147 – I suggest changing the term "client" for "patient"…

We thank the reviewer for this suggestion, and have changed our wording accordingly.

15. LINE 150 – Suppress "exploratory".

We deem it important to clearly stress the exploratory character of this study. Since the reviewer did not share their reasons for this comment, we have, for now, decided to refrain from suppressing ‘exploratory'.

16. LINE 151 – Although the proposal of look into Deserno et al. 2017 is interesting, that proposition would be more suitable for the reader if the article would be presented in a medium with hypertextual tools (which is not the case of PLOS ONE). So, explain very briefly, in the introduction, before presenting the aims, the domains of well-being as presented in Deserno et al., 2017.

We agree with the reviewer that it comes with disadvantages for the text‘s general comprehensibility to work with important information that is drawn from another article. We have, therefore, followed the reviewers‘ suggestion to add information on the ASD factors important to well-being from Deserno et al., 2017, see line 116-118.

17. LINES 155 – 156 – Why do the authors would like to explicitly mention the PCR as an objective of the paper, deviating from the core results? ("…provide an example of how to use the PCR toolbox to integrate the knowledge of clinicians in empirical studies"). Is it necessary? Wouldn't this interesting work itself be a proper example?

We thank the reviewer for this comment and have rephrased this aim without a focus on the PCR methodology: ‘...provide an example of how to integrate the knowledge of clinicians in empirical studies.‘

18. LINES 156 – 158 – More suitable for the "Methods section".

We thank the reviewer for this suggestion and have moved the sentence to the Methods section, lines 163 - 165.

19. LINES 159 – 167 – This paragraph would be unnecessary if the introductory section would be properly constructed. This last paragraph should be dedicated to the aims and objectives (that — in this article — were separated through the introduction. Moreover, sentences such as (LINES 164 – 165): "…the exact same variables found in Deserno et al. (2017)" should be avoided because the reader should have all the essential information for understanding this article without the necessity of reading additional articles. The last sentence should be placed in Methods.

We agree with the reviewer and have restructured the introductory section, by moving parts to the Methods section, and removing redundant paragraphs.

### METHODS

20. In general, this section is prolix, presents redundant parts, and is not straightforward. It needs to be reformulated to provide to the reader a better understanding of the step-by-step of what was done.

21. Regarding the PCR network, remember always use "'perceived causal' effect" instead of "causal effect", "causal network", "causal relation" etc (ex.; line 270, 272) since one perception does not indicate a real causal effect.

We have revisited every single use of the term ‘causal‘ to make sure it is clear that we talk about perceived causality, see our response to the reviewer comment no. 2.

22. Concerning the sample, how many clinicians were invited to participate, and how was the acceptance rate among all clinicians invited? How the authors calculated (and eventually decided) that the number of raters was enough (n=29)? How do the authors think about the number of responders in the follow-up phase?

As we sent out the survey through mailing lists of the clinical departments of the three mentioned institutions, we do not know how many clinicians have read the e-mail and decided not to respond. However, please note that whereas N=29 is a small sample size for quantitative statistics based on survey data (where each case represents one individual) the current sample size is particularly large for rater studies. Common rater samples often consist of less than 10 raters.

We chose to select a very specific sample since we wanted to select a group of experts in the autism field based on their extensive experience. The follow up intervention ratings were obtained from a subgroup of 16 participants as only 16 of 29 clinicians responded to this second online assessment (although we sent it to all 29 clinicians with multiple reminders). We regret to report that we are unable to assess more specific characteristics of those 13 clinicians not completing the follow up assessment regarding intervention targets–and how this affecting the results. We have added a more explicit comment on the potential bias due to attrition to the discussion, lines 595–597 (‘This leaves us unable to assess whether the subgroup asked to choose intervention targets has very specific or, instead, a diverse range of characteristics.‘).

We mention the limitations related to limited information on sample in lines 589 – 596: “Third, in this study we were able to reach out to highly experienced experts in the autism field: the knowledge that we combined into the PCR network was based on about 14 years, on average, of clinical work with people with an ASD diagnosis. This is a very specific sample of experts, of course, which limits the generalizability of the ratings to, for example, other mental health professionals or general practitioners (Nicolaidis et al., 2015). Also, although we know that the majority of our informants works for clinical institutes that have a tradition for over 40 years in specialized autism teams, we did not specifically ask for more detailed information about their background and type of experience.”

23. It is not clear how the selection of the institutions and the raters was done. Do the services investigated were the only facilities treating ASD in the region? Provide a glimpse both the selection of the sample. The simple mention that the authors "knew all the clinicians" is not descriptive enough and does not allow replications. Provide objective criteria.

Why the authors have included raters with different backgrounds (psychologists, psychiatrists, behavioral scientists, and social workers)? Since their formation is quite different from each other some bias on the appreciation of causal relations could certainly occur. Moreover, the PCR intends to access causal relations based on their clinical expertise. Even though the patients are the same, the clinical perception of a professional is biased by people's academic background.

In the same direction, mention that because they work in such facilities, "we know that all clinicians participating in the current study are involved in diagnostic work, consultation, and intervention services" is not informative enough. Besides that, how the authors guarantee that some of the respondents do not work only in the administration or coordination of such settings rather than as clinicians.

We distributed the questionnaire through the three leading clinical networks within the autism realm in the Netherlands, targeting specific individuals of which we knew that they are clinically speaking the authority in the Netherlands. Their affiliation with those institutions means that all clinicians participating in the current study are involved in diagnostic work, consultation, and intervention services - which is also the reason we selected these three leading clinical networks. Also, it is a Dutch standard that these institutes all work with multidisciplinary teams. We added this information in lines 167-176 of the manuscript. In addition, we assessed how many hours per week they engaged in clinical work and have added this information on page 13 of the revised manuscript. Although we know that the majority works for a clinical institute that have a tradition for over 40 years in specialized autism teams, we did not specifically ask for more detailed information about their background and type of experience. We, therefore, agree with the reviewer that there are some limitations due to the sample of the current study. We explicitly mention those limitations in line 589 - 598. Although individual differences related to professional background would be an interesting future research avenue, our intention was to find a way to find a meaningful way to represent clinical expert knowledge in network studies. For this purpose, it is beneficial to have a sample with a diverse background as this represents the range of expertise usually present at a clinical ‘case conference’, in Dutch clinics.

24.The authors should avoid expressions such as: "[they] consider 'advanced experience with ASD'". Such subjective evaluation weakens the methodology of the study. For example, mention only that "we knew that they are [specialists]" is too fragile. Objective criteria would be preferable.

We thank the reviewer for this comment. We consider these clinicians advanced, as the average clinical experience in the field was 14 years. We have now changed our wording to ‘years of experience’ in order to avoid subjective evaluations implied by ‘advanced‘, see lines 167.

25. What is CASS18+? I suppose it is an institution/facility for the treatment of ASD. Explain previously used abbreviations.

We thank the reviewer for pointing us to this missing definition, and have added it to the Method section. CASS18+ is a national network for professionals specialized in healthcare for autistic adults.

26. Provide a table with the demographic characteristics of the group.

We have provided all demographic information that we collected in lines 182 – 189 since we otherwise would have exceeded the Table limit of PLOS ONE. If the current editor is willing to make an exception in this case, we are happy to put the now typed out information into a table.

27. Provide to the reader a brief explanation of Deserno et al., 2017 and also Frewen et al., 2012 study. Despite hypertextual material is interesting, this sort of tool is not suitable for PLOS ONE and burdens the reader.

We give a short description of the Deserno et al., 2017 study lines 80-84 and have now added a visual step-by-step explanation of the study in the Figure added in the context of reviewer comment below (page 10 of this document). We do not deem it relevant to the current manuscript to explain the study Frewen et al. (2012) conducted regarding anxiety, PTSD and depression. We do mention the study in the context of the presented methodology in lines 146-149: “Recent studies implementing this scaling technique have used it to get a self-reported representation of symptom-to-symptom interactions administered to individuals experiencing symptoms related to posttraumatic stress and anxiety (Frewen et al., 2012; 2013), repetitive behaviors (Ruzzano et al., 2015) and posttraumatic stress and eating disorders (Thornley et al., 2016).“

28. LINES 204 to 210: That is "Statistical Analysis" rather than "Measure and Procedure".

We thank the reviewer for spotting this and have moved this information to the Statistical Analysis part.

29. FIGURE 1 and 2: The quality of the images does not allow us to read the texts inside the nodes or in the figure legends. Figure 2 needs a legend.

We regret to learn that the image quality was not sufficient. We will upload another format and hope that this will resolve the issue. Also, we have now added a legend to Figure 2.

30. LINES 243 – 247: The authors mention that: "Therefore, we decided to split the EMPIRICAL network in three (overlapping) parts with an (almost) equal number of nodes (j1= 14, j2=13, j3=13) based on their clustering in the EMPIRICAL network to ensure study feasibility."

Is that correct?

Yes, the reviewer is correct. We have changed the wording from empirical to ‘association network‘ now.

31. LINES 243 – 252: This was the less comprehensible part of the methodology. Reformulate it to allow a better understanding. It would help a lot providing a diagram/draw/scheme with the step-by-step of the methodology.

We thank the reviewer for this suggestion and have created a scheme of our methodology. We hope this helps to clarify what the reported results are based on:

32. LINES 250 – 252: "Large rater groups"? The author assessed only 29 raters… It is not clear what was done. The low number of raters is a serious question. And, when allocated to one of the three different groups, the number is even smaller (n=9, 10, and 10).

Please see our response to the reviewer‘s previous remark no. 23. We apologize for causing confusion: For a rater study, based on the literature of structured interviews, the current study‘s sample size can be considered large (de Villiers et al., 2005) while we, of course, are aware that for empirical studies based on survey data this is considered small. We have now clarified this with the addition of lines 570 -573.

33. LINE 259 – Why the authors have asked the raters to choose THREE targets? Based on what rationale?

This choice was based on the following rationale: We were aware that all factors included in the network somehow matter for well-being of autistic individuals, but we wanted the clinicians to make a selection of factors that they deem the most important intervention targets in the given network. In order to avoid the impression that we expected clinicians to rank-order the factors in the network, we limited the space of potential answers to three. The exact number of three answers was arbitrary but the goal was to limit the answers to avoid complete orderings of all factors. We have added this information to the manuscript in lines 260 - 261. Also, we planned to distribute the follow-up question regarding intervention targets in a second assessment round, i.e., after the rating assessments, as we wanted the intervention rating to be accompanied by a visual representation of the averaged clinician network. The intervention rating consisted of one simple question asking the participant to choose three intervention targets from the complete list of nodes depicted in the averaged clinician network.

34. LINE 276 – Why the authors have used a threshold of 6? Based on what rationale?

Please see our response to the related comment below, comment no. 37.

35. It also is not clear why, having in mind that the empirical network reflects partial correlations scaled between -1 and 1, why the authors have used clinicians' ratings from 0 to 10. Why not using a Likert scale, for example?

We thank the reviewer for the opportunity to clarify. Since the association network based on self-report data represents partial correlations, we decided on a continuous rating scale. An average of 7 on the rating scale would then be comparable to a partial correlation of 0.7.

36. Moreover, comparing two different networks with two different methodologies would not be problematic?

We regret to say that we are not sure what the reviewer is asking here. The two networks we compare are based on another assessment method, and therefore differ in some regards. We discuss this difference in the methods section (e.g., line 301-306) and have presented three different approaches to comparing these network structures, each focusing on different aspects of the networks (i.e. absence/presence of edges, weights, integration as a scaffolding structure in network estimation). In these steps, our methodology is completely transparent in what we are comparing.

37. I wonder if the authors could have further commentaries on the following sentence from LINES 276 and 277:

—"Manually thresholding the visual representation was necessary since the raters tended to attribute very high values to edges, they thought were present."

We thank the reviewer for pointing this out and have rephrased these sentences to avoid this confusion. We do specify a threshold in the visual representation of the network, i.e. the figure in the manuscript. Figure 2 depicts a regularized partial correlation network, which means that we used lasso regularization to control for Type 1 error, pushing small edges to zero. Since there is no similar technique for the rated network (as the connections are rated rather than estimated), we had to manually adjust for the fact that clinicians tended to attribute high values to edges they judged present (but not strong), resulting in a small, but high range, of edge weights. We did not, however, apply this threshold for further analyses. In the presented results all present edges are included, even if their average rating was smaller than 5. Furthermore, we do think that it was to be expected that the clinicians would rarely rate edges between these factors to be completely absent (i.e. 0). This is also in line with the theoretical assumption this whole endeavor is resting on: that these factors form a multicausal network of interrelations (see also our response to the last reviewer comment).

### RESULTS

38. The results section is difficult to follow and, sometimes, no distinguished from what should be in the discussion. The sentences are very long, and the excessive number of examples and comparisons with previous studies (that should be done in Discussion rather than in Results — E.g. LINE 339 – 343) makes it difficult for the reader to retain the main findings among the profusion of information.

We thank the reviewer for this suggestion and have reworked the results section with a focus on the main findings, moving the broader contextual information to the discussion section.

39. The authors do not present numerical results in this section. This data is important and not presented only as supplementary material (LINE 359).

We thank the reviewer for this suggestion and have moved this information from the supplement to the main text, see 372-375.

40. All tables and figures must be close to their title, footnotes, and all material referent to them. Is difficult to follow the results because the figures do not present this information. Moreover, the Figures do not have titles, but long explanations of what they represent. That fact indicates clearly that the figures are not self-explanatory as they supposed to be (at least in partly).The quality of the images is poor making it impossible to read them.

We regret to learn that the figure conversion tool of editorial manager has decreased the quality of our figures which had a high resolution. Now we have included even higher-resolution image for all figures in our resubmission. We hope this resolves the issue. We have revisited all Figure captions to improve their clarity:

41. LINE 337 – The authors should provide the list of abbreviations in the text and not only in the supplementary material.

We indeed solely provided an extensive list of the node abbreviations as supplementary material, but we agree with the reviewer that it is more convenient to have a Table next to the Figures listing all abbreviations used in the network and have, therefore, added such a list to the Figures directly (please see below).

42. The comprehension of the Tables and Figures should be independent of the text (and vice-versa). They have to contain all the necessary information for their comprehension (e.g. LINE 374 — the table title contains references to the text).

We thank the reviewer for this reminder and have adjusted all Figures and Tables to comply with the reviewer’s suggestion.

43. There is no reference in the text regarding where Table 1 should be placed. It was put in the text but not mentioned.

We thank the reviewer for spotting this, and have added a reference for Table 1, considering the reviewer’s earlier comment.

44. LINE 377 – 384: It is not clear why the authors decided to investigate the interventions the raters would do. That's not one of the aims of the study and with such a small number of raters, the results are not meaningful.

We understand the reviewer’s concern and hope that all the context provided above takes away the reviewer’s doubts regarding the sample size of the raters. We have now also added an explicit comment about this to the discussion section, see lines 582-585 and lines 610-612.

45. LINE 387 – 393 and 458 – 469: This paragraph is not part of the Results section. It describes the methodology.

We appreciate the reviewer’s note. We added these short summaries of the methodological steps at the beginning of each of the three sub-reports so the reader knows what the following results are based on. We have now deleted the reminder in lines 387-393 and have moved the information added in lines 458-469 to the Methods section, lines 320-329.

46. LINE 431: "…between -1 AND 1…"

We thank the reviewer and have corrected this typo.

47. Where is Figure 4 that is mentioned in the text?

We regret to learn that Figure 4 was not included in the reviewer’s manuscript version, and have thoroughly checked again that it is included in this submission.

48. LINE 496 – 499: This paragraph is not part of the Results section and should be placed in the Discussion section.

We appreciate the reviewer’s suggestion and have removed this paragraph from the Results section.

### DISCUSSION

49. In general, the Discussion is easier to read than the rest of the text, although repetitive. Nevertheless, the authors tend to stretch the line in some conclusions to explain some findings. For example, I did not understand the sentence in LINE 526-528: "This would be consistent with the plausible idea that ASD symptoms arise from sources external to the current networks (e.g., from problems associated with brain development)."

How's that, "external to the current network"? Are not all the ASD symptoms part of a neurodevelopmental problem?

We thank the reviewer for the opportunity to clarify. By ‘current networks’ we mean that there are more factors relevant to ASD than the ones included in the networks of this study. For example, early brain parameters are not assessed in the current study, but without any doubt relevant to atypical development (see Johnson et al., 2017 for an elaborate review of this line of thought). We have now explicitly added this information to line 545-549 in order to clarify what we mean by ‘external to the current network’. The reviewer’s second comment is in line with previous comments we have responded to in general comment no. 2. We have rephrased the sentence in line 563-565: “Third, we found that the way this clinicians sample perceive cause-effect relations between ASD symptoms, well-being, and domains of daily functioning is fairly similar to the interrelatedness of these factors found in self-reported data.“

50. Again, in Discussion, the problem of "causal" inferences persists.

Please see our response to the reviewer comment no. 2 above.

51. I suggest the following articles in order to improve the quality of the text:

A. Docherty M and Smith R. The case for structuring the discussion of scientific papers. BMJ 1999;318;1224-1225;

B. Horton R. The hidden research paper. ‭JAMA,‭ June‭ ‬5,‭ ‬2002‭—‬Vol‭ ‬287,‭ ‬No.‭ ‬21‬‬.‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

We thank the reviewer for suggesting these articles and have re-structured the discussion section of our manuscript according to the suggested structure as much as possible. We have moved the section discussing weaknesses of the study up so that the discussion of its relation to the broader literature comes just before the section discussing unanswered questions and future research.

52. LINE 523: Wouldn't be "…other than THE influence…" rather than "…other than THAT influence…"?‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

We thank the reviewer for spotting this and have changed our wording accordingly.

53. LINE 562: I do not think is fair the authors mention that they have used "large rater groups". The sample size is quite small and its subdivision smaller. This should be mentioned as a limitation.

Please see our response to similar comments above.

54. LINE 568: It is interesting the authors mention that "it is important to note that the centrality of nodes in the empirical network indicates the importance of a node…", but they relegate centrality measures to supplementary material.

See our response to an earlier comment by the reviewer, we have now moved this information to the main text.

55. LINE 572 – 573: This is one of the most important sentences in the manuscript that deserves more attention from the authors:

— "…assuming that the variables in question indeed form a causal system with symmetric effects…".

We would be keen to discuss this assumption more extensively with the reviewer and have now highlighted this again in the results section. Overall, we hope that our introduction of the network approach to psychology is a sufficient theoretical background for the reader to understand that this is the assumption under investigation.

56. LINE 608: "schematic representation of PERCEPTION OF cause-effect " rather than "schematic representation of cause-effect "‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

We have changed our wording accordingly: 'The schematic representation of perceived cause-effect models might benefit both clinician and client in any mental health setting (see also Kroeze, 2013).'

Attachment

Submitted filename: Response to Reviewers.pdf

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PLoS One. doi: 10.1371/journal.pone.0243298.r003

Decision Letter 1

Geilson Lima Santana

19 Nov 2020

Highways to happiness for autistic adults? Perceived causal relations among clinicians

PONE-D-20-15702R1

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PLoS One. doi: 10.1371/journal.pone.0243298.r004

Acceptance letter

Geilson Lima Santana

26 Nov 2020

PONE-D-20-15702R1

Highways to happiness for autistic adults? Perceived causal relations among clinicians

Dear Dr. Deserno:

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S1 Table. List of abbreviations of nodes in the networks.

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Data Availability Statement

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PERMALINK

Highways to happiness for autistic adults? Perceived causal relations among clinicians

Marie K Deserno

Denny Borsboom

Sander Begeer

Riet van Bork

Max Hinne

Hilde M Geurts

Roles

Abstract

Introduction

Network approach to psychological phenomena

Perceived causal pathways in the well-being system

Clinical validation of networks based on self-report data

Integration of clinical expertise into empirical network studies

The current study

Methods

Participants

Measures and procedure

Fig 1. Depiction of a previously published association network based on self-reported data from autistic adults.

Statistical analysis

(i) Perceived causal pathways in the well-being system

Fig 2. Diagram of the three analytic steps of the current study.

(ii) Clinical validation of networks based on self-reported data

(iii) Integration of clinical expertise into empirical network studies

Results

(i) Perceived causal pathways in the well-being system

Fig 3. This PCR network is a merged visualisation of three subnetworks based on ratings by three clinical expert groups and depicts the cause-effect ratings between ASD characteristics (blue), well-being domains (brown), and aspects of daily functioning (rose) as perceived by the clinicians.

Fig 4. Centrality indices for the PCR network depicted in Fig 3.

(ii) Clinical validation of networks based on self-reported data

Fig 5. This scatterplot depicts the relationship between the values of the PCR network and the association network.

Table 1. Contingency table for concordant and disconcordant positive (1), negative (-1) and absent (0) links in the PCR and the empirical network.

(iii) Integration of clinical expertise into empirical network studies

Fig 6. In the first row the three subnetworks are estimated with constraints based on the network structure found in the clinician network (PCR-constrained).

Subgraph 1

Subgraph 2

Subgraph 3

Discussion

Main findings

Limitations

Relation to the literature

Outlook

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Geilson Lima Santana

Roles

Author response to Decision Letter 0

Decision Letter 1

Geilson Lima Santana

Roles

Acceptance letter

Geilson Lima Santana

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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