Randomised controlled trial comparing narrative exposure therapy with present-centred therapy for older patients with post-traumatic stress disorder.

BACKGROUND: Evidence-based treatment and age-specific services are required to address the needs of trauma-affected older populations. Narrative exposure therapy (NET) may present an appropriate treatment approach for this population since it provides prolonged exposure in a lifespan perspective. As yet, however, no trial on this intervention has been conducted with older adults from Western Europe.AimsExamining the efficacy of NET in a sample of older adults. METHOD: Out-patients with post-traumatic stress disorder (PTSD), aged 55 years and over, were randomly assigned to either 11 sessions of NET (n = 18) or 11 sessions of present-centred therapy (PCT) (n = 15) and assessed on the Clinician-Administered PTSD Scale (CAPS) pre-treatment, post-treatment and at follow-up. Total scores as well as symptom scores (re-experience, avoidance and hyperarousal) were evaluated. RESULTS: Using a piecewise mixed-effects growth model, at post-treatment a medium between-treatment effect size for CAPS total score (Cohen's d = 0.44) was found, favouring PCT. At follow-up, however, the between-treatment differences were non-significant. Drop-out rates were low (NET 6.7%, PCT 14.3%) and no participant dropped out of the study because of increased distress. CONCLUSIONS: Both NET and PCT appear to be safe and efficacious treatments with older adults: PCT is non-intrusive and NET allows for imaginal exposure in a lifespan perspective. By selectively providing these approaches in clinical practice, patient matching can be optimised.Declaration of interestNone.

A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data.

OBJECTIVES: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. STUDY DESIGN AND SETTING: Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. RESULTS: The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. CONCLUSION: We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed.

Re-estimating sample size in cluster randomised trials with active recruitment within clusters.

Often only a limited number of clusters can be obtained in cluster randomised trials, although many potential participants can be recruited within each cluster. Thus, active recruitment is feasible within the clusters. To obtain an efficient sample size in a cluster randomised trial, the cluster level and individual level variance should be known before the study starts, but this is often not the case. We suggest using an internal pilot study design to address this problem of unknown variances. A pilot can be useful to re-estimate the variances and re-calculate the sample size during the trial. Using simulated data, it is shown that an initially low or high power can be adjusted using an internal pilot with the type I error rate remaining within an acceptable range. The intracluster correlation coefficient can be re-estimated with more precision, which has a positive effect on the sample size. We conclude that an internal pilot study design may be used if active recruitment is feasible within a limited number of clusters.

A comparison of methods to analyse continuous data from pseudo cluster randomized trials.

A major methodological reason to use cluster randomization is to avoid the contamination that would arise in an individually randomized design. However, when patient recruitment cannot be completed before randomization of clusters, the non-blindedness of recruiters and patients may cause selection bias, while in the control clusters, it may slow recruitment due to patient or recruiter preferences for the intervention. As a compromise, pseudo cluster randomization has been proposed. Because no insight is available into the relative performance of methods to analyse data obtained from this design, we compared the type I and II error rates of mixed models, generalized estimating equations (GEE) and a paired t-test to those of the estimator originally proposed in this design. The bias in the point estimate and its standard error were also incorporated into this comparison. Furthermore, we evaluated the effect of the weighting scheme and the accuracy of the sample size formula that have been described previously. Power levels of the originally proposed estimator and the unweighted mixed models were in agreement with the sample size formula, but the power of paired t-test fell short. GEE produced too large type I errors, unless the number of clusters was large (>30-40 per arm). The use of the weighting scheme generally enhanced the power, but at the cost of increasing the type I error in mixed models and GEE. We recommend unweighted mixed models as the best compromise between feasibility and power to analyse data from a pseudo cluster randomized trial.

A randomized clinical trial of a targeted intervention to moderate alcohol use and alcohol-related problems in at-risk adolescents.

This study investigated the effectiveness of a targeted intervention program aimed at at-risk adolescents in a randomized clinical trial design (N=107). This program combined intervention methods which have been proven effective in reducing drinking in young adults, such as an expectancy challenge, cognitive behavioral skill training and brief motivational feedback. Additionally, this intervention contained the new element of discussing biological, cognitive and social risk factors for developing alcohol problems. We investigated whether this seven session program was successful in changing cognitive determinants of drinking behavior and consequently in moderating alcohol use and the development of alcohol-related problems in at-risk adolescents. The intervention was effective in changing several of the targeted cognitive determinants. However, despite the changes in these cognitive determinants of drinking, the experimental group did not show a significant difference in decrease of drinking at posttest compared with the control group. The results did not yield support for any differential long term effects of the intervention. We concluded that although the present intervention successfully changed important cognitive determinants of drinking more is needed to change subsequent drinking behavior in at-risk adolescents.

Insect-pathogen dynamics: stage-specific susceptibility and insect density dependence.

The control of insect pests by using insect pathogens as dynamic biological control agents is a recent effort. Model studies on insect-pathogen relations can help in the development of biocontrol programs. Except for the work of Briggs and Godfray [1], insect-pathogen models ignore the stage-specific susceptibility of insects. Moreover most models do not incorporate insect self-regulation. We develop stage-structured models of insect-pathogen relations incorporating insect-density dependence and disease transmitted through direct contact between susceptible and infective individuals. The models are analyzed by using steady-state and stability analysis. Numerical solutions are used as sources of further insight into the dynamics of the insect-pathogen systems. It is shown that there are major differences in the dynamics of adult- and juvenile-infecting diseases. Moreover, the interplay between insect-density dependence and stage-specific susceptibility has important consequences for the dynamics of insect-pathogen systems.