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. Author manuscript; available in PMC: 2016 Feb 28.
Published in final edited form as: Psychiatry Res. 2014 Dec 15;225(3):395–401. doi: 10.1016/j.psychres.2014.12.006

Figure 1.

Figure 1

Latent class solution for co-morbid conditions in schizophrenia.

Co-morbid conditions indicate heterogeneity in schizophrenia (SZ). Latent class analysis (LCA), also referred to as latent class mixture modeling, is a multivariate statistical approach to heterogeneity in data. The figure shows the co-morbidity profile of the three latent classes of SZ patients. Latent Class 1(red line and ○ symbol) (54% of the sample) had a low probability of being diagnosed with a co-morbid psychiatric condition. Class 2 (green line and Δ symbol) (27% of the sample) consisted of SZ patients with low probability of endorsing alcohol or substance dependence, but alcohol abuse, substance abuse and cannabis use were prevalent. Class 3 (blue line and □ symbol) (19% of the sample) was SZ patients with a high probability of endorsing alcohol dependence and substance dependence. The x-axis indicates the co-morbid conditions included in the LCA. The y-axis represents the probability of endorsing co-morbid conditions scaled from 0% to 100%. ALCD, alcohol dependence; SUBD, dependence on illegal substances other than cannabis, CANNAB, cannabis abuse; ALCA, alcohol abuse; SUBA, substance abuse other than cannabis; DEP, major depressive disorder (according to DSM-IV).