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

The impact of personality disorder pathology on the effectiveness of Cognitive Therapy and Interpersonal Psychotherapy for Major Depressive Disorder

BACKGROUND: Despite extensive research, there is no consensus how Personality Disorders (PD) and PD features affect outcome for Major Depressive Disorder (MDD). The present study evaluated the effects of PD (features) on treatment continuation and effectiveness in Cognitive Therapy (CT) and Interpersonal Psychotherapy (IPT) for MDD. METHODS: Depressed outpatients were randomized to CT (n=72) and IPT (n=74). Primary outcome was depression severity measured repeatedly with the Beck Depression Inventory-II (BDI-II) at baseline, three months, at the start of each therapy session, at post-treatment and monthly during five months follow-up. RESULTS: Comorbid PD and PD features did not affect dropout. Multilevel and Cox regression models indicated no negative effect of PD on BDI-II change and remission rates during treatment and follow-up, irrespective of the treatment received. For both therapies, higher dependent PD features predicted overall lower BDI-II scores during treatment, however this effect did not sustain through follow-up. Cluster A PD features moderated treatment outcome during treatment and follow-up: individuals with high cluster A PD features had greater BDI-II reductions over time in CT as compared to IPT. LIMITATIONS: Not all therapists and participants were blind to the assessment of PD (features), and assessments were performed by one rater. Further research must investigate the state and trait dependent changes of PD and MDD over time. CONCLUSIONS: We found no negative impact of PD on the effectiveness and treatment retention of CT and IPT for MDD during treatment and follow-up. If replicated, cluster A PD features can be used to optimize treatment selection

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