Background:Most of the 13 542 trials contained in the Cochrane Schizophrenia Group’s register just tested the general efficacy of pharmacological or psychosocial interventions. Studies on the ...subsequent treatment steps, which are essential to guide clinicians, are largely missing. This knowledge gap leaves important questions unanswered. For example, when a first antipsychotic failed, is switching to another drug effective? And when should we use clozapine? The aim of this article is to review the efficacy of switching antipsychotics in case of nonresponse. We also present the European Commission sponsored “Optimization of Treatment and Management of Schizophrenia in Europe” (OPTiMiSE) trial which aims to provide a treatment algorithm for patients with a first episode of schizophrenia.Methods:We searched Pubmed (October 29, 2014) for randomized controlled trials (RCTs) that examined switching the drug in nonresponders to another antipsychotic. We described important methodological choices of the OPTiMiSE trial.Results:We found 10 RCTs on switching antipsychotic drugs. No trial was conclusive and none was concerned with first-episode schizophrenia. In OPTiMiSE, 500 first episode patients are treated with amisulpride for 4 weeks, followed by a 6-week double-blind RCT comparing continuation of amisulpride with switching to olanzapine and ultimately a 12-week clozapine treatment in nonremitters. A subsequent 1-year RCT validates psychosocial interventions to enhance adherence.Discussion:Current literature fails to provide basic guidance for the pharmacological treatment of schizophrenia. The OPTiMiSE trial is expected to provide a basis for clinical guidelines to treat patients with a first episode of schizophrenia.(Reprinted with permission from Schizophrenia Bulletin 2015; 41(3):549–558)
Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can ...arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large sample approximations which are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.
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