Stability of additive treatment effects in multiple treatment comparison meta-analysis: a simulation study

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.

Modeling Multicomponent Interventions in Network Meta-Analysis

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.

A review of methods for addressing components of interventions in meta-analysis

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder.

Joint modeling of concurrent binary outcomes in a longitudinal observational study using inverse probability of treatment weighting for treatment effect estimation

PURPOSE

Correlated healthcare utilization outcomes may be encoded as binary outcomes in epidemiologic studies. We demonstrate how to account for correlation between concurrent binary outcomes and confounding by person characteristics when estimating a treatment effect in observational studies.

METHODS

We present a joint shared-parameter model, weighted by inverse probability of treatment weights (IPTW) to account for confounding. The model is evaluated in a simulation study that emulates the Medical Expenditure Panel Survey data and compared with a covariate-adjusted joint model and with separate outcome models (IPTW weighted and covariate adjusted).

RESULTS

For the IPTW-weighted joint model, relative bias in the estimated treatment effect on outcome 1 ranged from -0.057 to -0.033 and outcome 2 from -0.077 to -0.043. For the covariate-adjusted joint model, relative bias ranged from -0.010 to -0.083 for outcome 1 and from -0.087 to -0.110 for outcome 2. The covariate-adjusted joint model estimated the effect more closely than the covariate-adjusted separate model. The IPTW-weighted joint model estimated the effect more closely for outcome 1.

CONCLUSIONS

The IPTW-weighted joint model handles correlation between binary outcomes, adjusts for confounding, and estimates the treatment effect accurately in observational studies. We illustrate the contribution of person-specific effects in estimating personalized risk.

Network meta-analysis of multicomponent interventions

In network meta‐analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta‐analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.

GetReal in network meta-analysis: a review of the methodology

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.

The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

Comparative effectiveness of monotherapies and combination therapies for patients with hypertension: protocol for a systematic review with network meta-analyses

BACKGROUND

Hypertension has been cited as the most common attributable risk factor for death worldwide, and in Canada more than one of every five adults had this diagnosis in 2007. In addition to different lifestyle modifications, such as diet and exercise, there exist many pharmaco-therapies from different drug classes which can be used to lower blood pressure, thereby reducing the risk of serious clinical outcomes. In moderate and severe cases, more than one agent may be used. The optimal mono- and combination therapies for mild hypertension and moderate/severe hypertension are unclear, and clinical guidelines provide different recommendations for first line therapy. The objective of this review is to explore the relative benefits and safety of different pharmacotherapies for management of non-diabetic patients with hypertension, whether of a mild or moderate to severe nature.

METHODS/DESIGN

Searches involving MEDLINE and the Cochrane Database of Systematic Reviews will be used to identify related systematic reviews and relevant randomized trials. The outcomes of interest include myocardial infarction, stroke, incident diabetes, heart failure, overall and cardiovascular related death, and important side effects (cancers, depression, syncopal episodes/falls and sexual dysfunction). Randomized controlled trials will be sought. Two reviewers will independently screen relevant reviews, titles and abstracts resulting from the literature search, and also potentially relevant full-text articles in duplicate. Data will be abstracted and quality will be appraised by two team members independently. Conflicts at all levels of screening and abstraction will be resolved through team discussion. Random effect pairwise meta-analyses and network meta-analyses will be conducted where deemed appropriate. Analyses will be geared toward studying treatment of mild hypertension and moderate/severe hypertension separately.

DISCUSSION

Our systematic review results will assess the extent of currently available evidence for single agent and multi-agent pharmacotherapies in patients with mild, moderate and severe hypertension, and will provide a rigorous and updated synthesis of a range of important clinical outcomes for clinicians, decision makers and patients.

TRIAL REGISTRATION

PROSPERO Registration Number: CRD42013004459.