Determining of XXXX prognosis using latent growth mixture models.

@entity1 @entity7804 @entity390 @entity391 @entity2704 @entity158 @entity10 @entity3917
BACKGROUND: This paper presents a retrospective study to classify @entity1 into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in @entity2704 prognosis. In the classical prospective clinical studies, @entity1 are classified with respect to baseline status and followed within a certain time period. However, latent growth mixture model is the most suitable method, which considers the population heterogenity and is not affected drop-outs if they are missing at random. Hence, we planned this comprehensive study to identify prognostic factors in @entity2704 . METHODS: The study data have been based on a 10-year computer-based follow-up data of Mersin University Headache Outpatient Department. The developmental trajectories within subgroups were described for the severity, frequency, and duration of @entity10 separately and the probabilities of each subgroup were estimated by using latent growth mixture models. SAS PROC TRAJ procedures, semiparametric and group-based mixture modeling approach, were applied to define the developmental trajectories. RESULTS: While the three-group model for the severity (mild, moderate, severe) and frequency (low, medium, high) of @entity10 appeared to be appropriate, the four-group model for the duration (low, medium, high, extremely high) was more suitable. The severity of @entity10 increased in the @entity1 with @entity390 , @entity391 , @entity3917 and @entity7804 . The frequency of @entity10 was especially related with increasing age and @entity158 . @entity390 and @entity3917 were also related with @entity10 duration. CONCLUSIONS: @entity390 , @entity391 and @entity3917 were the most significant factors to identify developmental trajectories. The remission time was not the same for the severity, frequency, and duration of @entity10 .
