Download Post Traumatic Stress Disorder as a Causal System

Post Traumatic Stress Disorder as a
Causal System
Nader Amir and Shaan McGhie
San Diego State University, San Diego, CA US.
Funding : This project was supported by Grants 1R01MH101118, R01
MH106477 from NIMH
Disclosure : Dr. Amir was formerly a part owner of Cognitive Retraining Technologies,
LLC (“CRT”), a company that marketed anxiety relief products. Dr. Amir’s ownership
interest in CRT was extinguished on January 29, 2016, when CRT was acquired by
another entity. Dr. Amir has an interest in royalty income generated by the
marketing of anxiety relief products by this entity.
Traditional models of Post Traumatic
Stress Disorder (PTSD)
• Latent construct causes the symptoms that quantify it
(DSM5; APA, 2013).
• That is, both categorical and dimensional models
conceptualize PTSD symptoms as indicators of an
underlying latent variable that is measured by these
less than psychometrically perfect indicators
• The goal is therefore:
– Make the indicator better: more psychometrically sound
Traditional models of Post Traumatic
Stress Disorder (PTSD)
• This has met with limited success
• We have yet to discover any pathonomonic
variables for the latent structures
• Maybe we need to look harder, better (more
biological) indicators
• Maybe the entire model can be complemented
Causal Systems Model of Post
Traumatic Stress Disorder (PTSD)
• Psychometricians have turned to a different
approach to understand mental disorders such as
PTSD
• Mental disorders as causal systems (Borsboom & Cramer, 2013)
graphical models for causal relations that can
complement conventional models (Greenland, Pearl, & Robins,
1999)
• Also called
– influence diagrams
– relevance diagrams
– causal networks
Causal System model of PTSD
• A stressor (trauma) causes a symptom, which
may cause other symptoms and in turn be
affected by those symptoms
• These symptoms themselves constitute the
mental disorder
Causal System model of PTSD
• McNally et al. (2014): Examined PTSD symptoms
in 362 earthquake survivors (38% met criteria for
probable PTSD)
• Feeling distant from other people was linked to
loss of interest in previously enjoyable activities
as well as emotional numbing
• Difficulty sleeping, hypervigilance, and being
easily startled were also clustered as interrelated
symptoms.
McNally et al (2014)
Current study
• Replicated and extended these network analyses
• Used a weighted and directed network
– The magnitude of the relation is shown through
thickness of the line
– The arrows start at the predictor symptom and end at
the predicted symptom
• Test the accuracy of the network
• Bayesian network
• The symptoms presented in the graphs
–
Distant = feeling of detachment from others
–
Numb = restricted affect
–
Future = sense of foreshortened future
–
Sleep = difficulty falling or staying asleep
–
Startle = exaggerated startled response
–
Lossint = diminished interest in previously enjoyable activities,
–
Avoidth = efforts to avoid thoughts that concern the trauma
–
Avoidact= efforts to avoid activities, places, or people that arouse recollections of the trauma.
–
Hyper = hyperarousal in response to cues
–
Dreams = traumatic dreams
–
Intrusion = intrusive thoughts, memories or images
–
Flash = Flashbacks of the trauma
–
Upset = feeling upset in response to reminders of the trauma
–
Concen = difficulty concentrating
–
Physior = Physiological reactions to reminders of the trauma
–
Anger = feeling irritable or having outbursts
Network Analyses
• Node
– A symptom in the graph
• Edge
– lines connecting nodes, indicating a relationship between two
nodes (thicker lines means stronger relationships/higher
correlations)
• Association Network
– simple correlations between symptoms
• Concentration Network
– Partial correlations between symptoms
• Relative Importance network
– Directed and weighted network (shows direction and magnitude
of relationships) of linear models.
– Uses lmg metric in R package Relaimpo (Grömping, 2006)
Measures of Centrality
• Betweeness
– number of times a node is on the shortest path
between two other nodes
• Closeness
– average distance of one node to the others (higher
number means closer together)
• Strength-out
– The effect of the node on other nodes
Results
665 students: 17 PTSD symptoms
• Mean PCL-C score: 32.12, SD = 12.38
• 15% (106) meet DSM-V criteria for PTSD
Fig 6. Association network of simple correlations between PTSD symptoms with a cutoff of
minimum .30 correlation.
Fig 8. Concentration network of partial correlations between PTSD symptoms with a
threshold of .10
Fig. 9 Relative Importance network of PTSD symptoms, showing direction and magnitude
of relationships
Centrality plot for the relative importance network.
Detecting communities of symptoms
• Possible to over-interpret the visualization of data
(Fried, 2016, Psych Network)
• Most studies use the Fruchterman-Reingold
algorithm to create a layout:
– Nodes with the most connections / highest number of
connections in the center of the graph
• Node placement just one of many equally
‘correct’ ways
Detecting communities of symptoms
Better ways:
– Use Eigen values
– Use spinglass algorithm detects communities
– Exploratory Graph Analysis (Golino & Epskamp (2016)
• currently under development
• Re-estimates a regularized partial correlation network
and uses the walktrap (a random walk) algorithm to
find communities
• unlike eigenvalue decomposition it shows directly what
items belong to what clusters
Fig. 11. Identify communities of items in networks using Exploratory Graph Analysis via
the R-package EGA and spinglass
But how sure are we?
Epskamp, Borsboom & Fried, 2016
• We can
– Estimate of the accuracy of edge-weights, by
drawing bootstrapped CI
– Investigate the stability of (the order of) centrality
indices after observing only portions of the data
– Perform bootstrapped difference tests between
edge-weights and centrality indices to test
whether these differ significantly from each other
Epskamp, Borsboom & Fried, 2016
• Bootstrapped confidence intervals of
estimated edge-weights for the estimated
network of 17 PTSD symptoms.
– The red line the sample values
– Grey area the bootstrapped CIs
– Each horizontal line represents one edge of the
network, ordered from the edge with the highest
edge-weight to the edge with the lowest edgeweight
Discussion
What does this imply?
• Epskamp et al found that: generally large bootstrapped CIs
imply that interpreting the order of most edges in the
network should be done with care and that
– upset when reminded of the trauma – upsetting thoughts/images
– being jumpy – being alert
– feeling distant – loss of interest
• are reliably the three strongest edges since their bootstrapped CIs do
not overlap with the bootstrapped CIs of any other edges
– Current study:
• None did not overlap
• Highest
– Hypervigilane- startle
– intrusion--dreams
– distant--numb
Centrality stability
• Stability of centrality indices by estimating
network models based on subsets of the data
Centrality stability
• Betweenness
• closeness
• strength
0.0505
0.0505
0.361
• The CS-coefficient indicates
–
–
–
–
betweenness (CS(cor = 0.7) = 0.05) and
closeness (CS(cor = 0.7) = 0.05) are not stable under subsetting cases.
Node strength performs better (CS(cor = 0.7) = 0.36)
but does not reach the cutoff of 0.5 from simulation studies
• Thus: order of node strength is interpretable with some care, while
the orders of betweenness and closeness are not
Testing for significant difference
• Edges cannot be shown to significantly differ
from one-another
Bayesian network
• McNally (in press)
– Parametric method that produces directed acyclical graphs
– Arrows with direction
– Lacks cycles (feedback loops)
– An interdisciplinary area the aim of determining causal
inferences from observational data. However, requires
additional assumptions (Peal, 2014)
– Possible? May be (smoking and cancer)
Bayesian Networks, with causal aspirations
Each node is printed in square
brackets along with all its
parents (which are reported
after a pipe as a colon-separated
list)
[flash]
[upset|flash]
[intrusion|upset]
[physior|upset]
[avoidth|upset]
[dreams|intrusion]
[avoidact|avoidth]
[amnesia|avoidth]
[lossint|avoidact]
[distant|lossint]
[numb|distant]
[concen|distant]
[hyper|distant]
[future|distant:numb]
[sleep|concen]
[anger|concen]
[startle|hyper]
Perturbed and restarted network
Discussion
• Replicated and extended the results of McNally et al.
(2014) in a larger sample
• These results suggest that in a large sample, PTSD
symptoms are interrelated especially bidirectionally
• These results suggest that the most central symptoms
may be the most important in the disorder and thus,
and ideal candidate to target in treatment
Discussion
• Interventions focusing on social support and
interaction for PTSD are likely to influence one
symptom cluster, thereby alleviating other
symptoms that it affects.
• These data suggest a potential causal system of
symptoms
• The fact that specific symptoms may give rise to
each other highlights a pattern that may lead to
their specific chronicity
Discussion
• However, the results of our estimating Network
accuracy suggested some caution
• Replication and larger samples
• Better characterized samples may provide more
clear and accurate networks
• Begin to create causal models that can be tested
Thank you