Analytical procedures used in the current study

2.3.2 Analytical procedures used in the current study

In this study the SANS/SAPS items were dichotomized into present/absent categories and LCA was conducted using SPSS (version 16.0 and add-on module).

2.3.2.1 SANS / SAPS Variables selected for the analysis

For the purposes of this study, only the following 12 SAPS / SANS global subscale scores or individual items were included in the latent class analysis. These included:

1. Eye contact,

2. Affective non-responsiveness, 3. Spontaneous movement, 4. Grooming,

5. Recreation.

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6. Auditory hallucinations, 7. Commenting voices, 8. Global hallucinations score 9. Thought insertion,

10. Control delusions, 11. Mind reading,

12. Global delusions score

The reasons for choosing these variables in the latent class analysis were:

1. In the original sib-pair study (Niehaus et al. 2005), a review of the available literature on subtyping in schizophrenia performed by the author identified these items as being relevant, and a theoretical basis for their selection was constructed for each item.

2. These 12 items showed high concordance rates in the sib-pair study (Niehaus et al. 2005). See Niehaus et al. (2005) for a description of the theoretical basis for the inclusion of these items in studies aiming to identify intermediate endophenotypes for schizophrenia.

2.3.2.2 Estimating the number of latent classes

The number of latent classes was estimated using the following model fit criteria: the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC) and the Likelihood ratio test.

Lower values of the AIC and BIC indicate the best fitting model. Large differences in BIC and AIC when comparing different models indicate good fit for the model with the lowest values.

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2.3.2.3 Subgroup comparisons

In this study we were interested in finding out whether there are differences between subgroups in the data. In this case we need to remember that the grouping variable will be an observed variable such as cannabis use or abuse or gender.

How do we interpret differences between the groups? LCA differs from FA in that it involves assigning subjects to distinct categories rather than identifying related variables and thus in LCA the item response probabilties may be identical between the groups and thus we can directly interpret the latent class prevalences because differences are quantitative and not qualitative.

2.3.2.4 Parameter estimation

We estimated different sets of parameters from the model. The Gamma parameters functioned as class membership probabilities, summing to 1 over the classes. Class membership probabilities estimate the proportion of the population falling in each class. The gamma parameters were calculated as functions of the covariates. The second set of parameters, the rho parameters, functioned as item-response probabilities conditional on latent class membership. The item response probabilities represent the probability of a particular variable (i.e., item in a questionnaire) being endorsed or manifested by an individual, given that he or she has been allocated to a specific latent class.

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2.3.2.5 Criteria for evaluating and labelling item-response probabilities

Homogeneity within a given class and separation (pattern of probabilities clearly differentiating among the latent classes) were the two criteria used for evaluating the overall pattern of item-response (conditional) probabilities. The beta parameters are logistic regression coefficients for the covariates, predicting class membership, where the last class is the reference class.

2.3.2.6 Effects of covariates and subgroups

We also wanted to investigate the effects of gender, cannabis, duration of illness (defined as period from first reported behavioural changes or psychosis to date of interview) and sib-pair status (whether or not subjects had an affected sib in the sample) on the model fit. For example, we wanted to test whether the proportion of persons in each latent class varied according to the level of a categorical group (e.g., gender) to which they belonged. We did this by restricting the parameters to be equal across the gender groups (i.e., male and female) in one model and not in another. The G² difference test was used to test the significance of the differences between the two models. A significant test would suggest that membership in the various latent classes varies by gender. These differences might manifest themselves in either the item-response probabilities or the latent class probabilities. If the item-response probabilities are equal across groups (i.e.

measurement invariance), it means that the latent class prevalences can be compared directly since the interpretation of the latent classes is identical across groups.

This phase of the study describes the application of latent class analysis (LCA) in a sample of 734 Xhosa-speaking schizophrenic subjects using factor analytically derived variables previously identified in an independent sample of this population (Niehaus et al. 2005). LCA was performed

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according to the abovementioned guidelines on the following 8 SANS and SAPS items identified by preliminary exploration of the data: eye contact, auditory hallucinations, global hallucinations score, global delusions score, grooming, affective non-responsiveness, spontaneous movement, and commenting voices.

However, in schizophrenia, as previously discussed, the debate about whether the underlying structure of schizophrenia is categorical or dimensional is an ongoing one. In the categorical view, schizophrenia can be represented as diagnostic categories that indicate a dichotomous view of ill versus healthy or belonging to subtype one or two. Some of the reasons for the popularity of this viewpoint are that it meets clinical needs and allows for ease of monitoring and health care planning for health authorities and insurers (Muthén 2006). Alternatively, schizophrenia can be considered dimensional in nature and as such each individual displays a certain amount (severity) of disease, thus producing a continuous distribution.

Given the previously discussed advantages of FMM (including the use of both a categorical and dimensional components) it may be an appropriate model to investigate the underlying structure of psychiatric conditions (Yung 1997, McLachlan & Peel 2000, Muthén 2006,). Despite this

advantage FMM has not yet been used extensively in this field (Muthén & Asparouhov 2006a, Muthén & Asparouhov 2006b). The main reasons for this might be that there is very little research on how a well-fitting model should be interpreted and even how this model should be applied in practice. It is however important that we take note of this method as the categorical versus dimensional debate is an ongoing issue in psychiatry and DSM is a good example of a shift from categorical (DSM-IV) to more dimensional (DSM-V to be released shortly).

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