Bayesian models for aggregate and individual patient data component network meta-analysis

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

BACKGROUND

Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA.

METHODS

We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities.

RESULTS

CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist.

CONCLUSIONS

CNMA methods are feasible for connected networks but questionable for disconnected networks.

Four 2×2 factorial trials of smartphone CBT to reduce subthreshold depression and to prevent new depressive episodes among adults in the community-RESiLIENT trial (Resilience Enhancement with Smartphone in LIving ENvironmenTs): a master protocol

INTRODUCTION

The health burden due to depression is ever increasing in the world. Prevention is a key to reducing this burden. Guided internet cognitive-behavioural therapies (iCBT) appear promising but there is room for improvement because we do not yet know which of various iCBT skills are more efficacious than others, and for whom. In addition, there has been no platform for iCBT that can accommodate ongoing evolution of internet technologies.

METHODS AND ANALYSIS

Based on our decade-long experiences in developing smartphone CBT apps and examining them in randomised controlled trials, we have developed the Resilience Training App Version 2. This app now covers five CBT skills: cognitive restructuring, behavioural activation, problem-solving, assertion training and behaviour therapy for insomnia. The current study is designed as a master protocol including four 2×2 factorial trials using this app (1) to elucidate specific efficacies of each CBT skill, (2) to identify participants' characteristics that enable matching between skills and individuals, and (3) to allow future inclusion of new skills. We will recruit 3520 participants with subthreshold depression and ca 1700 participants without subthreshold depression, to examine the short-term efficacies of CBT skills to reduce depressive symptoms in the former and to explore the long-term efficacies in preventing depression in the total sample. The primary outcome for the short-term efficacies is the change in depressive symptoms as measured with the Patient Health Questionnaire-9 at week 6, and that for the long-term efficacies is the incidence of major depressive episodes as assessed by the computerised Composite International Diagnostic Interview by week 50.

ETHICS AND DISSEMINATION

The trial has been approved by the Ethics Committee of Kyoto University Graduate School of Medicine (C1556).

TRIAL REGISTRATION NUMBER

UMIN000047124.

Graphical tools for visualizing the results of network meta-analysis of multicomponent interventions

Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face, remotely etc.), location (hospital, home etc.), provider (physician, nurse etc.), time of communication (synchronous, asynchronous etc.) and other context related components. Networks of multicomponent interventions are typically sparse and classical NMA inference is not straightforward and prone to confounding. Ideally, we would like to disentangle the effect of each component to find out what works (or does not work). To this aim, we propose novel ways of visualizing the NMA results, describe their use, and illustrate their application in real-life examples. We developed an R package viscomp to produce all the suggested figures.

Bayesian unanchored additive models for component network meta-analysis

Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a more restrictive form of additivity than the other which we term an anchored and unanchored model, respectively. We show that an anchored model can provide a poor fit to the data if it is misspecified. Second, given that Bayesian models are often preferred by practitioners, we develop two novel unanchored Bayesian CNMA models presented under the unified notation. An extensive simulation study examining bias, coverage probabilities, and treatment rankings confirms the favorable performance of the novel models. This is the first simulation study to compare the statistical properties of CNMA models in the literature. Finally, the use of our novel models is demonstrated on a real dataset, and the results of CNMA models on the dataset are compared.

Comparative effectiveness of oral antibiotics, probiotics, prebiotics, and synbiotics in the prevention of postoperative infections in patients undergoing colorectal surgery: A network meta-analysis

Oral antibiotics (OAB), probiotics, prebiotics, and synbiotics are reported to be effective for preventing postoperative infection following colorectal surgery, but the comparative effectiveness between them has not been studied. To compare these interventions through a network meta-analysis. Ovid Medline, Embase, and the Cochrane Controlled Register of Trials (CENTRAL) were searched from inception to January 1, 2022 without any language restriction. Two reviewers independently screened the retrieved articles, assessed risk of bias, and extracted information from the included randomised controlled trials (RCTs). The primary outcome was infection rate, and the secondary outcome was anastomotic leakage rate. 4322 records were retrieved after literature search, and 20 RCTs recruiting 3726 participants were finally included. The analysis showed that usual care (UC) + Synbiotics ranked the most effective treatment (SUCRA = 0.968), UC + OAB ranked the second (SUCRA = 0.797), and UC + IAB ranked the third (SUCRA = 0.678) for preventing postoperative infection rate, but only UC + OAB achieved statistical significance. UC + OAB was the most effective treatment (SUCRA = 0.927) for preventing anastomotic leakage rate. Our study confirmed that preoperative administration of OAB was associated with lower infection rate and anastomotic leakage rate than placebo and UC alone. However, the beneficial effect of probiotics and synbiotics should still be investigated by large-scale randomised controlled trials.