Effectiveness of pharmacological and psychological interventions for treating post-traumatic stress disorder in adults with childhood abuse: protocol for a systematic review and network meta-analysis

Introduction Post-traumatic stress disorder (PTSD) is a debilitating mental disorder that develops after being exposed to a traumatic event. PTSD is common among adults who have experienced physical/sexual childhood abuse. Several psychological and pharmacological interventions are used for treating PTSD in this particular group, and it is important to identify what interventions, whether alone or in combination with other treatments, are more effective compared with others. Therefore, this review aims to provide synthesis of evidence on the effectiveness of different interventions used for treating PTSD following childhood abuse. Methods and analysis Electronic search will be conducted using different databases such as PubMed, EMBASE, PsycINFO to identify randomised controlled trials (RCTs) used for assessing interventions for PTSD following childhood abuse. Data on treatment effectiveness for PTSD with childhood abuse and other variables will be extracted from each paper and reported as appropriate. Extracted effect-size estimates will be combined using Bayesian network meta-analysis (NMA). Risk of bias will be assessed through the Cochrane Collaboration tool for RCTs tool. NMA assumptions (heterogeneity, transitivity, inconsistency) will be assessed and reported. Meta-regression and subgroup analyses will be performed to explore and explain possible sources of heterogeneity. Ethics and dissemination This research is based on literature review and does not require the approval of ethical board as it does not involve dealing with humans or animals. Findings of this review will be published in a peer-reviewed journal. PROSPERO registration number CRD42020207409.

Automated summary

This review aims to synthesize evidence of the effectiveness of different interventions for treating PTSD following childhood abuse. Bayesian network meta-analysis will be used to combine effect-size estimates, and meta-regression and subgroup analyses will be performed to explore possible sources of heterogeneity.