Evidence-based Clinical Practice – Lecture 6 (UNIVERSITY OF AMSTERDAM)

There are three types of theories:

1. Focusing on manifest variables
   This entails focusing on the variables that can be observed (e.g. a person is distracted and has trouble organizing tasks).
2. Focusing on latent variables
   This entails focusing on the variables that are indirectly observed (e.g. based on other variables.) (e.g. a person has ADHD).
3. Focusing on relations between manifest variables (i.e. network models)
   This entails focusing on the relations between the variables that can be observed (e.g. a person is distracted and thus has trouble organizing tasks).

The DSM-5 makes use of theories based on latent variables.

A theory focusing on manifest variables has the problem of whether there is a cut-off or not (e.g. a person is distracted or not). Besides that, if there is a cut-off, the question is what the cut-off is. Lastly, there is a problem of whether the treatment should be per manifest variable or not.

A theory focusing on latent variables has the problem of whether the latent variable is nominal, continuous or both (e.g. a person has ADHD or does not have ADHD). Besides that, it is the question of whether a latent variable should be based on consensus or on empirical evidence. Lastly, it is the question whether the treatment should focus on the latent variable or on the manifest variables (i.e. the symptoms). This regards to whether symptoms are treated or whether the presumed underlying cause is treated.

An empirical latent variable model requires a sample of the entire population including an enormous variety of symptoms.

Disadvantages of nominal latent variables based on consensus are that there is high comorbidity between disorders (1), there are arbitrary limits (i.e. why the cut-off for ADHD at 6/9 symptoms and not 5/9) (2) and there is a lot of heterogeneity within groups (3).

Basing a latent variable on empirical evidence can make use of a mixture analysis (1), factor analysis (2) or a factor mixture analysis (3). Typically, these three types of models are being fitted to the data and then it is determined which model fits the data best. This is then used in the explanation of the latent variable. The factor loading refers to how close the line is to the original variable.

A nominal model that fits best according to empirical data has the advantages that there is no comorbidity because there are only subgroups of the latent variable (e.g. ADHD subgroup with anxiety symptoms and ADHD subgroup that is distracted). There are no arbitrary limits as the limits are obtained from the data. In addition to that, there is reduced heterogeneity within groups because the subgroups of the latent variable are by definition homogenous.

The network model of psychopathology states that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. The causality hypothesis holds that when causal relations among symptoms are strong, the onset of one symptom will lead to the onset of others. The connectivity hypothesis states that a strongly interconnected symptom network is vulnerable to a contagion effect of spreading activation through the network. This means that widespread symptom activation as a result of an external stressor can lead to symptoms persisting when the initiating stressor is removed.

The centrality hypothesis states that highly central symptoms have greater potential to spread symptom activation throughout the network than symptoms on the periphery. The comorbidity hypothesis states that symptoms can occur in multiple disorders and that some symptoms can thus bridge different disorders.

A mental disorder is characterized by both the state and the structure of the network. A mental disorder could be characterized as a state of harmful equilibrium. Greater connectivity may confer either risk or a greater probability of symptom persistence.

Node strength refers to the summed, absolute strength of a node’s direct link. The DSM-5 has most likely not captured all symptoms of a disorder and has not necessarily identified the most important symptoms.

The momentary perspective states that symptoms are aggregates of moment-to-moment experiences. According to this perspective, these experiences constitute the true building blocks of psychopathology. This highlights the importance of understanding the chronometry of experiences, symptoms and disorders.

There are several problems with a network model:

1. The question whether the manifest variables are nominal or continuous.
2. The question of whether networks should be based on consensus or empirical evidence.
3. The question of whether networks should be based on cross-sectional or longitudinal data.
4. The question of whether every person has their own network.
5. The question of whether key symptoms or the connectivity of the network should be treated.

There is a conditional positive manifold for most disorders. This states that symptoms of a disorder tend to be positively interconnected even after controlling for shared variance among symptoms. This suggests meaningful clustering of symptoms in syndromes.

More insight into the structure of psychopathology may improve diagnosis and thus treatment efficacy.

The mixture analysis regards empirical classification into groups. The purpose of mixture- and cluster analysis is to determine latent groups. There are within-group similarities but between-group differences. The difference between mixture analysis and cluster analysis is that mixture analysis is a statistical method whereas cluster analysis is not.

There are several steps in cluster analysis:

1. Selection of sample (based on theory)
2. Selection of variables (based on theory)
3. Calculate inequalities between participants
4. Do the cluster analysis
5. Interpretation of the cluster analysis
6. Choosing the number of clusters.
7. Validation of final cluster solution (e.g. replication; test differences between clusters on variables not used in the cluster analysis).

Conducting the cluster analysis consists of choosing the number of clusters (1), SPSS estimating the initial clusters (2), each participant is added to the nearest cluster (3), new cluster centres are calculated (4) and then phase 3 and phase 4 are repeated until there are no changes in step 4 (5).

The interpretation is based on the means in each cluster. There is no statistical method to choose the number of clusters in the cluster analysis. Choosing the number of clusters can be done by running cluster analyses with varying number of clusters. Next, a graph can be made with number of clusters on the x-axis and the average Euclidean distance to cluster centre on y-axis. The inflexion point can then be checked and this determines the number of clusters.