1,722,216 research outputs found
Step by step trial data, Syrian displaced people in Lebanon
No full textThis contains the data collected in the randomised trial examining the effects of the digital intervention Step by Step on depression in Syrian displaced people in Lebano
A Guided Digital Health Intervention for Depression in Lebanon: Randomized Trial
No full textThis contains the data collected in the randomised trial examining the effects of the digital intervention Step by Step on depression in people living in Lebano
Data from the randomised trial on Step by step for Lebanese citizens
No full textThese data are from a randomised trial on the intervention "Step by Step" for depression in people living in Lebanon
How to prove that your therapy is effective, even when it is not: A guideline.
No full textAims. Suppose you are the developer of a new therapy for a mental health problem or you have several years of experience working with such a therapy, and you would like to prove that it is effective. Randomised trials have become the gold standard to prove that interventions are effective, and they are used by treatment guidelines and policy makers to decide whether or not to adopt, implement or fund a therapy. Methods. You would want to do such a randomised trial to get your therapy disseminated, but in reality your clinical experience already showed you that the therapy works. How could you do a trial in order to optimise the chance of finding a positive effect? Results. Methods that can help include a strong allegiance towards the therapy, anything that increases expectations and hope in participants, making use of the weak spots of randomised trials (risk of bias), small sample sizes and waiting list control groups (but not comparisons with existing interventions). And if all that fails one can always not publish the outcomes and wait for positive trials. Conclusions. Several methods are available to help you show that your therapy is effective, even when it is not
Sponsorship bias in the comparative efficacy of psychotherapy and pharmacotherapy for adult depression: Meta-analysis
No full textFrom living systematic reviews to meta-analytical research domains
No full textBecause of the rapidly increasing number of randomised controlled trials (RCTs) and meta-analyses in many fields, there is an urgent need to step up from meta-analyses to higher levels of aggregation of outcomes of RCTs. Network meta-analyses and umbrella reviews allow higher levels of aggregation of RCT outcomes, but cannot adequately cover the evidence for a whole field. The 'Meta-Analytic Research Domain' (MARD) may be a new methodology to aggregate RCT data of a whole field. A MARD is a living systematic review of a research domain that cannot be covered by one PICO. For example, a MARD of psychotherapy for depression covers all RCTs comparing the effects of all types of psychotherapy to control conditions, to each other, to pharmacotherapy and combined treatment. It also covers all RCTs comparing treatment formats, the effects in different target groups, subtypes of depression and secondary outcomes. Although the time and resources needed to build a MARD are considerable, they offer many advantages, including a comprehensive and consistent overview of a research field and important meta-analytic studies that cannot be conducted with conventional methods. MARDs are a promising method to step up the aggregation of RCTs to a next level and it is highly relevant to work out the methods of this approach in a more detailed way
Efficacy of cognitive bias modification interventions in anxiety and depression: meta-analysis
Full text linkBackground Cognitive bias modification (CBM) interventions are strongly advocated in research and clinical practice
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