Title: How perceived causal networks can complement case conceptualization, diagnostic classification, and data-based networks: An introduction to a method for constructing personalized networks.
Guidelines to use PECAN
Definition of PECAN:
- An idiographic model of an individual’s psychopathology of their perceived causal relations (PCR) between the nodes in a system. This model entails:
- node selection (which nodes are relevant?)
- causal ratings of relations between combination of th eselected nodes
- The visualization of this model in the form of a personalized network or aggregated group network consisting of nodes (e.g., symptoms) and directed edges between nodes (arrows) representing the PCRs. Optionally, further analyses of features such as centrality, density, or feedback loops within these networks can be conducted.
Data collection
Selecting nodes:
- Completely personalized: good for idiographic but difficult to aggregate
- Predetermined List of nodes: should encompass the most prevalent problems in the target population
- Predetermined list that is individualized not more than 6 nodes.
Node properties: what you may assess with VAS. These could be:
- Frequency
- Severity
- Modifiability
- Controllability
Assessing edges: Frequency: How often do you feel sad, if you talked to your mother? Certainty: Do you think your sadness is caused by talking to your mother? Counterfactuals: If you no longer talked to your mother, would you still feel sad?
Analysis
Visualization Visualization of inidividual network is easy: these are the things to consider:
- present all nodes
- node size and node color can be used to represent node properties (node size descrives frequency, node color denotes severity)
- edge quantification can be drawn with varying widths or color shades Visualization of aggregated networks is harder. Consider which nodes are relevant for your hypothesis. You can choose to exclude nodes when aggregating networks.
Simplification You should measure centrality with the original edges but for presentation it could be useful to show the most important edged: number of edges equals the number of nodes.
PECAN2 can be used to analyze on R.
Description of the network The two important variables are: Out-degree centrality: “how much each node in the network influences other nodes in the network through one-step edges. It is defined as the number of outgoing edges of a given node.” In-degree centrality: “how much each node in the network is influenced by the other nodes. It is defined as the number of incoming edges of a given node.” Intresting can be in individual loops the amount of feedback loops.
Effects of the Data Collection and Visualization The resulting network and presenting it to the clients can have negative effects (so overwhelming), positive effects (gave insights), clinical utility (ideas for behavioral change), what might be missing (a node? an edge?).
Challenges of PECAN
Recall bias and self-report issue. Bias depends on availability of symptoms (severe symptoms are more salient), distance to the period rated (recency bias), current state at the time of rating. PECAN may work worse for people with disorders characterized by poor insight. How much does the person have to know about causality, symptoms and network theory? It may be important to explain symtpoms/processes. How can we use the insights of other relevant people for the patient to validate networks?
I see many problems in using PECAN with PBT. How can you depict the how and why of processes within this framework? Rumination: how interacts rumination with other processes? Why is rumination chosen as strategy to reach a goal? Which goal is this person using?