A systematic review of network meta-analyses for pharmacological treatment of common mental disorders. Issue 1 (12th January 2018) - Williams, Taryn; Stein, Dan J; Ipser, Jonathan
Abstract : Question: Network meta-analyses (NMAs) of treatment efficacy across different pharmacological treatments help inform clinical decision-making, but their methodological quality may vary a lot depending also on the quality of the included primary studies. We therefore conducted a systematic review of NMAs of pharmacological treatment for common mental disorders in order to assess the methodological quality of these NMAs, and to relate study characteristics to the rankings of efficacy and tolerability. Study selection and analysis: We searched three databases for NMAs of pharmacological treatment used in major depression, generalised anxiety disorder (GAD), social anxiety disorder (SAD), post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD) and specific phobia. Studies were appraised using the International Society for Pharmacoeconomics and Outcomes Research checklist of good research practices for indirect-treatment-comparison and network-meta-analysis studies. Findings: Twenty NMAs were eligible for inclusion. The number of randomised controlled trials per NMA ranged from 11 to 234, and included between 801 to more than 26 000 participants. Overall, antidepressants were found to be efficacious and tolerable agents for several disorders based on rankings (45%) or statistical significance (55%). The majority of NMAs in this review adhered to guidelines by including a network diagram (70%), assessing consistency (75%), making use of a random effects model (75%), providing information on the model used to fit the data (75%) and adjusting for covariates (75%). Conclusions: The 20 NMAs of depression and anxiety disorders, PTSD and/or OCD included in this review demonstrate some methodological strengths in comparison with the larger body of published NMAs for medical disorders, support current treatment guidelines and help inform clinical decision-making.