Background Antipsychotic treatment is associated with metabolic disturbance. However, the degree to which metabolic alterations occur in treatment with different antipsychotics is unclear. Predictors of metabolic dysregulation are poorly understood and the association between metabolic change and change in psychopathology is uncertain. We aimed to compare and rank antipsychotics on the basis of their metabolic side-effects, identify physiological and demographic predictors of antipsychotic-induced metabolic dysregulation, and investigate the relationship between change in psychotic symptoms and change in metabolic parameters with antipsychotic treatment.Methods We searched MEDLINE, EMBASE, and PsycINFO from inception until June 30, 2019. We included blinded, randomised controlled trials comparing 18 antipsychotics and placebo in acute treatment of schizophrenia. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in body weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride, and glucose concentrations. We did meta-regressions to examine relationships between metabolic change and age, sex, ethnicity, baseline weight, and baseline metabolic parameter level. We examined the association between metabolic change and psychopathology change by estimating the correlation between symptom severity change and metabolic parameter change. FindingsOf 6532 citations, we included 100 randomised controlled trials, including 25 952 patients. Median treatment duration was 6 weeks (IQR 6-8). Mean differences for weight gain compared with placebo ranged from −0•23 kg (95% CI −0•83 to 0•36) for haloperidol to 3•01 kg (1•78 to 4•24) for clozapine; for BMI from −0•25 kg/m² (−0•68 to 0•17) for haloperidol to 1•07 kg/m² (0•90 to 1•25) for olanzapine; for total-cholesterol from −0•09 mmol/L (-0•24 to 0•07) for cariprazine to 0•56 mmol/L (0•26-0•86) for clozapine; for LDL cholesterol from −0•13 mmol/L (−0.21 to −0•05) for cariprazine to 0•20 mmol/L (0•14 to 0•26) for olanzapine; for HDL cholesterol from 0•05 mmol/L (0•00 to 0•10) for brexpiprazole to −0•10 mmol/L (−0•33 to 0•14) for amisulpride; for triglycerides from −0•01 mmol/L (−0•10 to 0•08) for brexpiprazole to 0•98 mmol/L (0•48 to 1•49) for clozapine; for glucose from −0•29 mmol/L (−0•55 to −0•03) for lurasidone to 1•05 mmol/L (0•41 to 1•70) for clozapine. Greater increases in glucose were predicted by higher baseline weight (p=0•0015) and male sex (p=0•0082). Non-white ethnicity was associated with greater increases in total cholesterol (p=0•040) compared with white ethnicity. Improvements in symptom severity were associated with increases in weight (r=0•36, p=0•0021), BMI (r=0•84, p<0•0001), totalcholesterol (r=0•31, p=0•047), and LDL cholesterol (r=0•42, p=0•013), and decreases in HDL cholesterol (r=−0•35, p=0•035).Interpretation Marked differences exist between antipsychotics in terms of metabolic side-effects, with olanzapine and clozapine exhibiting the worst profiles and aripiprazole, brexpiprazole, caripraz...

Individual participant data (IPD) meta‐analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta‐analysis (IPD‐MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD‐MA using evidence from clinical trials or non‐randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD‐MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.

IntroductionAlthough guided forms of internet-based cognitive–behavioural therapy (iCBT) result in a substantial reduction in depression, it seems that the most scalable way to deliver iCBT is without guidance. However, direct evidence on the comparison between guided and self-guided iCBT is scarce. Moreover, it is unclear which types of patients may benefit more from each of these two forms of iCBT. Network meta-analysis (NMA) using individual participant data (IPD) offers a way to assess the relative efficacy of multiple (>2) interventions. Moreover, it maximises our power to detect patient-level characteristics (covariates) that have an important effect on the efficacy of interventions. This protocol describes the procedures of an IPD-NMA, which aims at examining the relative efficacy of guided compared with self-guided iCBT and at identifying predictors and moderators of treatment outcome.Methods and analysisWe will use an existing database on psychotherapies for adult depression to identify eligible studies. This database has been updated up to 1 January 2018, through literature searches in PubMed, Embase, PsycINFO and Cochrane Library. The outcome of this IPD-NMA is reduction in depressive symptoms severity. We will fit the model in a Bayesian setting. After fitting the model, we will report the relative treatment effects for different types of patients, and we will discuss the clinical implications of our findings. Based on the results from the IPD-NMA model, we will develop and validate a personalised prediction model, aiming to provide patient-level predictions about the effects of the interventions.Ethics and disseminationAn ethical approval is not required for this study. The results will be published in a peer-review journal. These results will guide clinical decisions about the most efficient way to allocate iCBT resources, thereby increasing the scalability of this innovative therapeutic approach.