A comparison of 7 random-effects models for meta-analyses that estimate the summary odds ratio

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis

Combining intensive practice nurse counselling or brief general practitioner advice with varenicline for smoking cessation in primary care: study protocol of a pragmatic randomized controlled trial

Introduction: Combining behavioural support and pharmacotherapy is most effective for smoking cessation and recommended in clinical guidelines. Despite that smoking cessation assistance from the general practitioner can be effective, dissemination of clinical practice guidelines and efforts on upskilling has not lead to the routine provision of smoking cessation advice among general practitioners. Intensive counselling from the practice nurse could contribute to better smoking cessation rates in primary care. However, the effectiveness of intensive counselling from a practice nurse versus usual care from a general practitioner in combination with varenicline is still unknown. Materials and methods: A pragmatic randomized controlled trial was conducted comparing: (a) intensive individual counselling delivered by a practice nurse and (b) brief advice delivered by a general practitioner; both groups received 12-weeks of open-label varenicline. A minimum of 272 adult daily smoking participants were recruited and treated in their routine primary care setting. The primary outcome was defined as prolonged abstinence from weeks 9 to 26, biochemically validated by exhaled carbon monoxide. Data was analysed blinded according to the intention-to-treat principle and participants with missing data on their smoking status at follow-up were counted as smokers. Secondary outcomes included: one-year prolonged abstinence, short-term incremental cost-effectiveness, medication adherence, and baseline predictors of successful smoking cessation. Discussion: This trial is the first to provide scientific evidence on the effectiveness, cost-effectiveness, and potential mechanisms of action of intensive practice nurse counselling combined with varenicline under real-life conditions. This paper explains the methodology of the trial and discusses the pragmatic and/or explanatory design aspects

Identifying effective behavioural components of Intervention and Comparison group support provided in SMOKing cEssation (IC-SMOKE) interventions: a systematic review protocol

BACKGROUND: Systematic reviews of behaviour change interventions for smoking cessation vary in scope, quality, and applicability. The current review aims to generate more accurate and useful findings by (1) a detailed analysis of intervention elements that change behaviour (i.e. behaviour change techniques (BCTs)) and potential moderators of behaviour change (i.e. other intervention and sample characteristics) and (2) assessing and controlling for variability in support provided to comparison groups in smoking cessation trials. METHODS: A systematic review will be conducted of randomized controlled trials of behaviour change interventions for smoking cessation in adults (with or without pharmacological support), with a minimum follow-up of 6 months, published after 1995. Eligible articles will be identified through the Cochrane Tobacco Addiction Group Specialized Register. Study authors will be asked for detailed descriptions of smoking cessation support provided to intervention and comparison groups. All data will be independently coded by two researchers. The BCT taxonomy v1 (tailored to smoking cessation interventions) and template for intervention description and replication criteria will be used to code intervention characteristics. Data collection will further include sample and trial characteristics and outcome data (smoking cessation rates). Multilevel mixed-effects meta-regression models will be used to examine which BCTs and/or BCT clusters delivered to intervention and comparison groups explain smoking cessation rates in treatment arms (and effect sizes) and what key moderators of behaviour change are. Predicted effect sizes of each intervention will be computed assuming all interventions are compared against comparison groups receiving the same levels of behavioural support (i.e. low, medium, and high levels). Multi-disciplinary advisory board members (policymakers, health care providers, and (ex-)smokers) will provide strategic input throughout the project to ensure the review's applicability to policy and practice. DISCUSSION: By capturing BCTs in intervention and comparison groups, this systematic review will provide more accurate estimates of the effectiveness of smoking cessation interventions, the most promising BCTs and/or BCT clusters associated with smoking cessation rates in intervention and comparison arms, and important moderators of behaviour change. The results could set new standards for conducting meta-analyses of behaviour change interventions and improve research, service delivery, and training in the area of smoking cessation

Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study

Background: Systematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure. However, the standard meta-analysis of odds ratios using a random-effects model has a number of potential problems. An attractive alternative approach for the meta-analysis of binary outcomes uses a class of generalized linear mixed models (GLMMs). GLMMs are believed to overcome the problems of the standard random-effects model because they use a correct binomial-normal likelihood. However, this belief is based on theoretical considerations, and no sufficient simulations have assessed the performance of GLMMs in meta-analysis. This gap may be due to the computational complexity of these models and the resulting considerable time requirements. Methods: The present study is the first to provide extensive simulations on the performance of four GLMM methods (models with fixed and random study effects and two conditional methods) for meta-analysis of odds ratios in comparison to the standard random effects model. Results: In our simulations, the hypergeometric-normal model provided less biased estimation of the heterogeneity variance than the standard random-effects meta-analysis using the restricted maximum likelihood (REML) estimation when the data were sparse, but the REML method performed similarly for the point estimation of the odds ratio, and better for the interval estimation. Conclusions: It is difficult to recommend the use of GLMMs in the practice of meta-analysis. The problem of finding uniformly good methods of the meta-analysis for binary outcomes is still open

Longitudinal associations between depression and diabetes complications: a systematic review and meta-analysis

Aims. To conduct a systematic review and meta-analysis of longitudinal studies assessing the bi- directional association between depression and diabetes macrovascular and microvascular complications. Methods. Embase, Medline, and PsycINFO databases were searched from inception through 27th November 2017. A total of 4,592 abstracts were screened for eligibility. Meta-analyses used multilevel random/mixed-effects models. Quality was assessed using the Newcastle-Ottawa scale. Results. 22 studies were included in the systematic review. 16 studies examined the relationship between baseline depression and incident diabetes complications, of which nine studies involving over 1 million participants were suitable for meta-analysis. Depression was associated with an increased risk for incident macrovascular (Hazard Ratio HR=1.38; 95%CI: 1.30-1.47) and microvascular disease (HR=1.33; 95%CI: 1.25-1.41). Six studies examined the association between baseline diabetes complications and subsequent depression, of which two involving over 230 000 participants were suitable for meta-analysis. The results showed that diabetes complications increased the risk of incident depressive disorder (HR=1.14; 95%CI: 1.07-1.21). The quality analysis showed increased risk of bias notably in the representativeness of selected cohorts and ascertainment of exposure and outcome. Conclusions. Depression in people with diabetes is associated with an increased risk of incident macrovascular and microvascular complications. The relationship between depression and diabetes complications appears bi-directional. However, the risk of developing diabetes complications in depressed people is higher than the risk of developing depression in people with diabetes complications. The underlying mechanisms warrant further research