Cognitive-behaviour therapy for post-traumatic stress in schizophrenia. A randomized controlled trial.

BACKGROUND: There is limited evidence for effective interventions in the treatment of post-traumatic stress symptoms within individuals diagnosed with schizophrenia. Clinicians have concerns about using exposure treatments with this patient group. The current trial was designed to evaluate a 16-session cognitive restructuring programme, without direct exposure, for the treatment of post-traumatic stress symptoms specifically within individuals diagnosed with schizophrenia. METHOD: A multicentre randomized controlled single-blinded trial with assessments at 0 months, 6 months (post-treatment) and 12 months (follow-up) was conducted. A total of 61 participants diagnosed with schizophrenia and exhibiting post-traumatic stress symptoms were recruited. Those randomized to treatment were offered up to 16 sessions of cognitive-behaviour therapy (CBT, including psychoeducation, breathing training and cognitive restructuring) over a 6-month period, with the control group offered routine clinical services. The main outcome was blind rating of post-traumatic stress symptoms using the Clinician Administered PTSD Scale for Schizophrenia. Secondary outcomes were psychotic symptoms as measured by the Positive and Negative Symptom Scale and the Psychotic Symptom Rating Scale. RESULTS: Both the treatment and control groups experienced a significant decrease in post-traumatic stress symptoms over time but there was no effect of the addition of CBT on either the primary or secondary outcomes. CONCLUSIONS: The current trial did not demonstrate any effect in favour of CBT. Cognitive restructuring programmes may require further adaptation to promote emotional processing of traumatic memories within people diagnosed with a psychotic disorder.

Extending the Lincoln-Petersen estimator for multiple identifications in one source.

The Lincoln-Petersen estimator is one of the most popular estimators used in capture-recapture studies. It was developed for a sampling situation in which two sources independently identify members of a target population. For each of the two sources, it is determined if a unit of the target population is identified or not. This leads to a 2 × 2 table with frequencies f11 ,f10 ,f01 ,f00 indicating the number of units identified by both sources, by the first but not the second source, by the second but not the first source and not identified by any of the two sources, respectively. However, f00 is unobserved so that the 2 × 2 table is incomplete and the Lincoln-Petersen estimator provides an estimate for f00 . In this paper, we consider a generalization of this situation for which one source provides not only a binary identification outcome but also a count outcome of how many times a unit has been identified. Using a truncated Poisson count model, truncating multiple identifications larger than two, we propose a maximum likelihood estimator of the Poisson parameter and, ultimately, of the population size. This estimator shows benefits, in comparison with Lincoln-Petersen's, in terms of bias and efficiency. It is possible to test the homogeneity assumption that is not testable in the Lincoln-Petersen framework. The approach is applied to surveillance data on syphilis from Izmir, Turkey.

A likelihood ratio approach to family-based association studies with covariates.

We introduce a procedure for association based analysis of nuclear families that allows for dichotomous and more general measurements of phenotype and inclusion of covariate information. Standard generalized linear models are used to relate phenotype and its predictors. Our test procedure, based on the likelihood ratio, unifies the estimation of all parameters through the likelihood itself and yields maximum likelihood estimates of the genetic relative risk and interaction parameters. Our method has advantages in modelling the covariate and gene-covariate interaction terms over recently proposed conditional score tests that include covariate information via a two-stage modelling approach. We apply our method in a study of human systemic lupus erythematosus and the C-reactive protein that includes sex as a covariate.