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Objective: To compare the cost‐effectiveness of different treatments for cervical intraepithelial neoplasia (CIN). Design: A cost‐effectiveness analysis based on data available in the literature and expert opinion. Setting: England. Population: Women treated for CIN. Methods: We developed a decision‐analytic model to simulate the clinical course of 1000 women who received local treatment for CIN and were followed up for 10 years after treatment. In the model we considered surgical complications as well as oncological and reproductive outcomes over the 10‐year period. The costs calculated were those incurred by the National Health Service (NHS) of England. Main outcome measures: Cost per one CIN2+ recurrence averted (oncological outcome); cost per one preterm birth averted (reproductive outcome); overall cost per one adverse oncological or reproductive outcome averted. Results: For young women of reproductive age, large loop excision of the transformation zone (LLETZ) was the most cost‐effective treatment overall at all willingness‐to‐pay thresholds. For postmenopausal women, LLETZ remained the most cost‐effective treatment up to a threshold of £31,500, but laser conisation became the most cost‐effective treatment above that threshold. Conclusions: LLETZ is the most cost‐effective treatment for both younger and older women. However, for older women, more radical excision with laser conisation could also be considered if the NHS is willing to spend more than £31,500 to avert one CIN2+ recurrence. [ABSTRACT FROM AUTHOR]

When studies use different scales to measure continuous outcomes, standardised mean differences (SMD) are required to meta‐analyse the data. However, outcomes are often reported as endpoint or change from baseline scores. Combining corresponding SMDs can be problematic and available guidance advises against this practice. We aimed to examine the impact of combining the two types of SMD in meta‐analyses of depression severity. We used individual participant data on pharmacological interventions (89 studies, 27,409 participants) and internet‐delivered cognitive behavioural therapy (iCBT; 61 studies, 13,687 participants) for depression to compare endpoint and change from baseline SMDs at the study level. Next, we performed pairwise (PWMA) and network meta‐analyses (NMA) using endpoint SMDs, change from baseline SMDs, or a mixture of the two. Study‐specific SMDs calculated from endpoint and change from baseline data were largely similar, although for iCBT interventions 25% of the studies at 3 months were associated with important differences between study‐specific SMDs (median 0.01, IQR −0.10, 0.13) especially in smaller trials with baseline imbalances. However, when pooled, the differences between endpoint and change SMDs were negligible. Pooling only the more favourable of the two SMDs did not materially affect meta‐analyses, resulting in differences of pooled SMDs up to 0.05 and 0.13 in the pharmacological and iCBT datasets, respectively. Our findings have implications for meta‐analyses in depression, where we showed that the choice between endpoint and change scores for estimating SMDs had immaterial impact on summary meta‐analytic estimates. Future studies should replicate and extend our analyses to fields other than depression. [ABSTRACT FROM AUTHOR]

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two‐stage network meta‐regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non‐randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta‐regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re‐analysis of a network of studies comparing three drugs for relapsing–remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non‐randomized evidence. [ABSTRACT FROM AUTHOR]

Background: The development of vaccine candidates for COVID‐19, and the administration of booster vaccines, has meant a significant reduction in COVID‐19 related deaths world‐wide and the easing of global restrictions. However, new variants of SARS‐CoV‐2 have emerged with less susceptibility to vaccine induced immunity leading to breakthrough infections among vaccinated people. It is generally acknowledged that immunoglobulins play the major role in immune‐protection, primarily through binding to the SARS‐COV‐2 receptor binding domain (RBD) and thereby inhibiting viral binding to the ACE2 receptor. However, there are limited investigations of anti‐RBD isotypes (IgM, IgG, IgA) and IgG subclasses (IgG1–4) over the course of vaccination and breakthrough infection. Method: In this study, SARS‐CoV‐2 humoral immunity is examined in a single subject with unique longitudinal sampling. Over a two year period, the subject received three doses of vaccine, had two active breakthrough infections and 22 blood samples collected. Serological testing included anti‐nucleocapsid total antibodies, anti‐RBD total antibodies, IgG, IgA, IgM and IgG subclasses, neutralization and ACE2 inhibition against the wildtype (WT), Delta and Omicron variants. Results: Vaccination and breakthrough infections induced IgG, specifically IgG1 and IgG4 as well as IgM and IgA. IgG1 and IgG4 responses were cross reactive and associated with broad inhibition. Conclusion: The findings here provide novel insights into humoral immune response characteristics associated with SARS‐CoV‐2 breakthrough infections. [ABSTRACT FROM AUTHOR]

In network meta‐analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non‐randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta‐regression (NMR) models allowing for cross‐design and cross‐format synthesis. The models integrate a three‐level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias‐adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing–remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta‐regression showed that intervention efficacy decreases with increasing participant age. We also re‐analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re‐analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within‐study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics. [ABSTRACT FROM AUTHOR]

In a quantitative synthesis of studies via meta‐analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta‐analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data‐generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta‐analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency. [ABSTRACT FROM AUTHOR]

Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision‐making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta‐analysis framework. We propose a two‐stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta‐analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta‐regression model. We apply the approach to a network meta‐analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing‐remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high‐risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low‐risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach. [ABSTRACT FROM AUTHOR]

Background: Network meta‐analysis (NMA) produces complex outputs as many comparisons between interventions are of interest. The estimated relative treatment effects are usually displayed in a forest plot or in a league table and several ranking metrics are calculated and presented. Methods: In this article, we estimate relative treatment effects of each competing treatment against a fictional treatment of average performance using the "deviation from the means" coding that has been used to parametrize categorical covariates in regression models. We then use this alternative parametrization of the NMA model to present a ranking metric (PreTA: Preferable Than Average) interpreted as the probability that a treatment is better than a fictional treatment of average performance. Results: We illustrate the alternative parametrization of the NMA model using two networks of interventions, a network of 18 antidepressants for acute depression and a network of four interventions for heavy menstrual bleeding. We also use these two networks to highlight differences among PreTA and existing ranking metrics. We further examine the agreement between PreTA and existing ranking metrics in 232 networks of interventions and conclude that their agreement depends on the precision with which relative effects are estimated. Conclusions: A forest plot with NMA relative treatment effects using "deviation from means" coding could complement presentation of NMA results in large networks and in absence of an obvious reference treatment. PreTA is a viable alternative to existing probabilistic ranking metrics that naturally incorporates uncertainty. [ABSTRACT FROM AUTHOR]

Meta‐analysis results are usually presented in forest plots, which show the individual study results and the summary effect along with their confidence intervals. In this paper, we propose a system of linear springs as a mechanical analogue of meta‐analysis that enables visualization and enhances intuition. The length of a spring corresponds to a study treatment effect and the stiffness of the spring corresponds to its inverse variance. To synthesize study springs we use two main operations: connection in parallel and connection in series. We show the equivalence between meta‐analysis and linear springs for fixed effect and random effects pairwise meta‐analysis and we also derive indirect treatment effects. We use examples to illustrate the different meta‐analytical schemes using the corresponding system of springs. The proposed visualization can serve as an educational tool, especially useful for researchers with no statistical background. The analogy between meta‐analysis and springs facilitates intuition for notions such as heterogeneity and the differences between fixed and random effects meta‐analysis. [ABSTRACT FROM AUTHOR]

Network meta‐analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every‐day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision‐making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application. [ABSTRACT FROM AUTHOR]

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta‐analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double‐blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all‐cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta‐Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two‐dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. Highlights: Network meta‐analysis is considered to be a proper way of demonstrating the available evidence, since it allows comparisons between multiple interventions, and has been proved to be statistically powerful.It is challenging to present the voluminous results of NMA in an efficient and comprehendible manner.Evidence evolution based on the relatively new method NMA has not been investigated yet.The results of NMA should not only include the effects but also the confidence in the evidence, which can help interpret the findings appropriately.Effective use of rapidly developing statistical analysis and presentation tools such as Shiny package in R, may facilitate and simplify the visualization of NMA output.We should stay conservative towards new drugs, as their performance was often shown to be exaggerated initially, and it took time to become stable. [ABSTRACT FROM AUTHOR]

Objectives: Symptoms of insomnia are highly prevalent in the elderly. A significant number of pharmacological and non‐pharmacological interventions exist, but, up‐to‐date, their comparative efficacy and safety has not been sufficiently assessed. Methods: We integrated the randomized evidence from every available treatment for insomnia in the elderly (>65 years) by performing a network meta‐analysis. Several electronic databases were searched up to May 25, 2019. The two primary outcomes were total sleep time and sleep quality. Data for other 6 efficacy and 8 safety outcomes were also analyzed. Results: Fifty‐three RCTs with 6832 participants (75 years old on average) were included, 43 of which examined the efficacy of one or more drugs. Ten RCTs examined the efficacy of non‐pharmacological interventions and were evaluated only with pairwise meta‐analyses because they were disconnected from the network. The overall confidence in the evidence was very low primarily due to the small amount of data per comparison and their sparse connectedness. Several benzodiazepines, antidepressants, and z‐drugs performed better in both primary outcomes, but few comparisons had data from more than one trial. The limited evidence on non‐pharmacological interventions suggested that acupressure, auricular acupuncture, mindfulness‐based stress reduction program, and tart cherry juice were better than their control interventions. Regarding safety, no clear differences were detected among interventions due to large uncertainty. Conclusions: Insufficient evidence exists on which intervention is more efficacious for elderly patients with insomnia. More RCTs, with longer duration, making more direct interventions among active treatments and presenting more outcomes are urgently needed. [ABSTRACT FROM AUTHOR]

The physiological stress response to surgery promotes wound healing and functional recovery and includes the activation of neural, inflammatory and proangiogenic signaling pathways. Paradoxically, the same pathways also promote metastatic spread and growth of residual cancer. Human and animal studies show that cancer surgery can increase survival, migration and proliferation of residual tumor cells. To secure the survival and growth of disseminated tumor cells, the formation of premetastatic niches in target organs involves a complex interplay between microenvironment, immune system, circulating tumor cells, as well as chemical mediators and exosomes secreted by the primary tumor. This review describes the current understanding of the early mechanisms of dissemination, as well as how surgery may facilitate disease progression. [ABSTRACT FROM AUTHOR]

Background: In fixed‐dose antidepressant trials, the lower range of the licensed dose achieves the optimal balance between efficacy and tolerability. Whether flexible upward titration while side‐effects permit provides additional benefits is unknown. Methods: We did a systematic review of placebo‐controlled randomized trials that examined selective serotonin reuptake inhibitors (SSRIs), venlafaxine or mirtazapine in the acute treatment of major depression. Our primary outcome was response, defined as 50% or greater reduction in depression severity. Secondary outcomes included drop‐outs due to adverse effects and drop‐outs for any reason. We conducted random‐effects meta‐analyses to calculate the ratios of odds ratios (RORs) between trials comparing the flexible dose titrating above the minimum licensed dose against placebo and those comparing the fixed minimum licensed dose against placebo. Results: We included 123 published and unpublished randomized controlled trials (29 420 participants). There was no evidence supporting efficacy of the flexible dosing over the fixed low dose of SSRIs (ROR 0.96, 95% CI: 0.73 to 1.25), venlafaxine (1.24, 0.96 to 1.60) or mirtazapine (0.77, 0.33 to 1.78). No important differences were noted for tolerability or for any subgroup analyses except the superior efficacy of venlafaxine flexible dosing between 75 and 150 mg over the fixed 75 mg (1.30, 1.02 to 1.65). Conclusion: There was no evidence to support added value in terms of efficacy, tolerability or acceptability of flexibly titrating up the dosage over the minimum licensed dose of SSRIs or mirtazapine. For venlafaxine, increased efficacy can be expected by flexibly titrating up to 150 mg. [ABSTRACT FROM AUTHOR]

One of the key features of network meta‐analysis is ranking of interventions according to outcomes of interest. Ranking metrics are prone to misinterpretation because of two limitations associated with the current ranking methods. First, differences in relative treatment effects might not be clinically important and this is not reflected in the ranking metrics. Second, there are no established methods to include several health outcomes in the ranking assessments. To address these two issues, we extended the P‐score method to allow for multiple outcomes and modified it to measure the mean extent of certainty that a treatment is better than the competing treatments by a certain amount, for example, the minimum clinical important difference. We suggest to present the tradeoff between beneficial and harmful outcomes allowing stakeholders to consider how much adverse effect they are willing to tolerate for specific gains in efficacy. We used a published network of 212 trials comparing 15 antipsychotics and placebo using a random effects network meta‐analysis model, focusing on three outcomes; reduction in symptoms of schizophrenia in a standardized scale, all‐cause discontinuation, and weight gain. [ABSTRACT FROM AUTHOR]

Network meta‐analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user‐friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta‐Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One, 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv). Six domains that affect the level of confidence in the NMA results are considered: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open‐source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study‐level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to "no concerns," "some concerns," or "major concerns" in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating ("high," "moderate," "low," or "very low"). In conclusion, the user‐friendly web‐based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions. [ABSTRACT FROM AUTHOR]

Differences between arm-based (AB) and contrast-based (CB) models for network meta-analysis (NMA) are controversial. We compare the CB model of Lu and Ades (2006), the AB model of Hong et al(2016), and two intermediate models, using hypothetical data and a selected real data set. Differences between models arise primarily from study intercepts being fixed effects in the Lu-Ades model but random effects in the Hong model, and we identify four key difference. (1) If study intercepts are fixed effects then only within-study information is used, but if they are random effects then between-study information is also used and can cause important bias. (2) Models with random study intercepts are suitable for deriving a wider range of estimands, eg, the marginal risk difference, when underlying risk is derived from the NMA data; but underlying risk is usually best derived from external data, and then models with fixed intercepts are equally good. (3) The Hong model allows treatment effects to be related to study intercepts, but the Lu-Ades model does not. (4) The Hong model is valid under a more relaxed missing data assumption, that arms (rather than contrasts) are missing at random, but this does not appear to reduce bias. We also describe an AB model with fixed study intercepts and a CB model with random study intercepts. We conclude that both AB and CB models are suitable for the analysis of NMA data, but using random study intercepts requires a strong rationale such as relating treatment effects to study intercepts. [ABSTRACT FROM AUTHOR]

The Mantel-Haenszel (MH) method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto's odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesizing, eg, data on mortality or serious adverse events. In this setting, sparse data occur often and yet there is to-date, no extension of the MH method for the case of NMA. In this paper, we fill this gap by presenting a MH-NMA method for odds ratios. Similarly to the pairwise MH method, we assume common treatment effects. We implement our approach in R, and we provide freely available easy-to-use routines. We illustrate our approach using data from two previously published networks. We compare our results to those obtained from three other approaches to NMA, namely, NMA with noncentral hypergeometric likelihood, an inverse-variance NMA, and a Bayesian NMA with a binomial likelihood. We also perform simulations to assess the performance of our method and compare it with alternative methods. We conclude that our MH-NMA method offers a reliable approach to the NMA of binary outcomes, especially in the case or sparse data, and when the assumption of methodological and clinical homogeneity is justifiable. [ABSTRACT FROM AUTHOR]

Meta‐analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random‐effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method—frequently used as the default method for meta‐analyses with random effects—has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random‐effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real‐life data studies. [ABSTRACT FROM AUTHOR]

Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision‐making, particularly when data from systematic reviews of primary studies do not adequately answer a research question. However, systematic reviewers and guideline developers may struggle with using the results of modeling studies, because, at least in part, of the lack of a common understanding of concepts and terminology between evidence synthesis experts and mathematical modellers. The use of a common terminology for modeling studies across different clinical and epidemiological research fields that span infectious and non‐communicable diseases will help systematic reviewers and guideline developers with the understanding, characterisation, comparison, and use of mathematical modeling studies. This glossary explains key terms used in mathematical modeling studies that are particularly salient to evidence synthesis and knowledge translation in clinical medicine and public health. [ABSTRACT FROM AUTHOR]

Summary: Background: Patient‐reported outcomes are important in the assessment of efficacy of intervention for ulcerative colitis (UC). Aim: To compare the impact of interventions for moderate‐to‐severe UC on health‐related quality of life (HRQL). Methods: We searched Medline, Embase, CENTRAL and grey literature sources through October 2017. We included randomised controlled trials (RCTs) that compared infliximab, adalimumab, golimumab, vedolizumab or tofacitinib to each other or placebo. Outcomes included the change in quality of life scores and the proportion of patients with improvement in quality of life. We performed random‐effect pairwise and network meta‐analysis. We assessed confidence in estimates using the CINeMA (Confidence in Network Meta‐Analysis) framework. Results: Fourteen RCTs assessed HRQL using the Inflammatory Bowel Disease Questionnaire (IBDQ) (14 trials), the Short Form questionnaire‐36 (SF‐36) (seven trials) or the European Quality of Life‐5 Dimensions questionnaire (EQ‐5D) (three trials). At induction (13 trials), low to very low confidence evidence suggested that all agents significantly improved both generic and disease‐specific HRQL scores compared to placebo. However, only infliximab (MD 18.58; 95% CI 13.19‐23.97) and vedolizumab (MD 18.00; 95% CI 11.08‐24.92) showed clinically meaningful improvement in IBDQ score. Differences among individual interventions were imprecise. For maintenance (four trials), very low confidence evidence suggested that vedolizumab, tofacitinib and adalimumab maintained improvement in HRQL. Conclusions: Induction treatment with infliximab, adalimumab, golimumab, vedolizumab or tofacitinib improves quality of life compared to placebo. Evidence on maintenance therapy is sparse and uncertain. Head‐to‐head comparisons could enhance confidence in conclusions about differences between drugs in terms of HRQL. [ABSTRACT FROM AUTHOR]

The protective effect in rainbow trout ( Oncorhynchus mykiss) of an experimental subunit vaccine targeting antigens in the parasite Ichthyophthirius multifiliis has been evaluated and compared to effects elicited by a classical parasite homogenate vaccine. Three recombinant parasite proteins (two produced in E. coli and one in insect cells) were combined and injected i.p., and subsequently, protection and antibody responses were analysed. Both the experimental and the benchmark vaccine induced partial but significant protection against I. multifiliis when compared to control fish. Specific antibody responses of vaccinated trout (subunit vaccine) were raised against one neurohypophysial n-terminal domain protein #10 of three recombinant proteins, whereas the benchmark vaccine group showed specific antibody production against all three recombinant proteins. The immunogenic parasite protein #10 may be a potential vaccine candidate supplementing the protective I-antigen in future vaccine trials. [ABSTRACT FROM AUTHOR]

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Non-randomized studies aim to reveal whether or not interventions are effective in real-life clinical practice, and there is a growing interest in including such evidence in the decision-making process. We evaluate existing methodologies and present new approaches to using non-randomized evidence in a network meta-analysis of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non-randomized studies in a network meta-analysis of RCTs: the naïve data synthesis, the design-adjusted synthesis, the use of non-randomized evidence as prior information and the use of three-level hierarchical models. We apply some of the methods in two previously published clinical examples comparing percutaneous interventions for the treatment of coronary in-stent restenosis and antipsychotics in patients with schizophrenia. We discuss in depth the advantages and limitations of each method, and we conclude that the inclusion of real-world evidence from non-randomized studies has the potential to corroborate findings from RCTs, increase precision and enhance the decision-making process. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time-dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When there are multiple competing interventions for a healthcare problem, the design of new studies could be based on the entire network of evidence as reflected in a network meta-analysis. There is a practical need to answer how many (if any) studies are needed, of which design (i.e., which treatments to compare), and with what sample size to infer conclusively about the relative treatment effects of a set of target or all competing treatments and their relative ranking. We consider the precision in the results obtained from network meta-analysis: the precision of the joint distribution of the estimated basic parameters of the model and the precision in the treatment ranking. We quantify the precision in the estimated effects by considering their variance-covariance matrix and estimate the precision in ranking by quantifying the dissimilarity of the density functions of summary effect estimates. Then, based on a desirable improvement in precision, we calculate the required sample size for each possible study design and number of study arms, and we present visual tools that can help trialists select the optimal study design. We use a published network of interventions for the treatment of hepatocellular carcinoma to illustrate the suggested methodology. The presented methodology can aid investigators making informed and evidence-based decisions about planning new studies. [ABSTRACT FROM AUTHOR]

Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples. [ABSTRACT FROM AUTHOR]

Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance, has been long challenged. Our aim is to identify known methods for estimation of the between-study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between-study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between-study variance. Based on the scenarios and results presented in the published studies, we recommend the Q-profile method and the alternative approach based on a 'generalised Cochran between-study variance statistic' to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence-based recommendations require an extensive simulation study where all methods would be compared under the same scenarios. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd Legal Statement. [ABSTRACT FROM AUTHOR]

Objectives The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field. Methods Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by three Cochrane Review Groups relating to mental health were included. Results One hundred ninety systematic reviews were considered. Missing outcome data were present in at least one included study in 175 systematic reviews. Of these 175 systematic reviews, 147 (84%) accounted for missing outcome data by considering a relevant primary or secondary outcome (e.g., dropout). Missing outcome data implications were reported only in 61 (35%) systematic reviews and primarily in the discussion section by commenting on the amount of the missing outcome data. One hundred forty eligible meta-analyses with missing data were scrutinized. Seventy-nine (56%) of them had studies with total dropout rate between 10 and 30%. One hundred nine (78%) meta-analyses reported to have performed intention-to-treat analysis by including trials with imputed outcome data. Sensitivity analysis for incomplete outcome data was implemented in less than 20% of the meta-analyses. Conclusions Reporting of the techniques for handling missing outcome data and their implications in the findings of the systematic reviews are suboptimal. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Missing outcome data are commonly encountered in randomized controlled trials and hence may need to be addressed in a meta-analysis of multiple trials. A common and simple approach to deal with missing data is to restrict analysis to individuals for whom the outcome was obtained (complete case analysis). However, estimated treatment effects from complete case analyses are potentially biased if informative missing data are ignored. We develop methods for estimating meta-analytic summary treatment effects for continuous outcomes in the presence of missing data for some of the individuals within the trials. We build on a method previously developed for binary outcomes, which quantifies the degree of departure from a missing at random assumption via the informative missingness odds ratio. Our new model quantifies the degree of departure from missing at random using either an informative missingness difference of means or an informative missingness ratio of means, both of which relate the mean value of the missing outcome data to that of the observed data. We propose estimating the treatment effects, adjusted for informative missingness, and their standard errors by a Taylor series approximation and by a Monte Carlo method. We apply the methodology to examples of both pairwise and network meta-analysis with multi-arm trials. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Missing outcome data are a problem commonly observed in randomized control trials that occurs as a result of participants leaving the study before its end. Missing such important information can bias the study estimates of the relative treatment effect and consequently affect the meta-analytic results. Therefore, methods on manipulating data sets with missing participants, with regard to incorporating the missing information in the analysis so as to avoid the loss of power and minimize the bias, are of interest. We propose a meta-analytic model that accounts for possible error in the effect sizes estimated in studies with last observation carried forward (LOCF) imputed patients. Assuming a dichotomous outcome, we decompose the probability of a successful unobserved outcome taking into account the sensitivity and specificity of the LOCF imputation process for the missing participants. We fit the proposed model within a Bayesian framework, exploring different prior formulations for sensitivity and specificity. We illustrate our methods by performing a meta-analysis of five studies comparing the efficacy of amisulpride versus conventional drugs (flupenthixol and haloperidol) on patients diagnosed with schizophrenia. Our meta-analytic models yield estimates similar to meta-analysis with LOCF-imputed patients. Allowing for uncertainty in the imputation process, precision is decreased depending on the priors used for sensitivity and specificity. Results on the significance of amisulpride versus conventional drugs differ between the standard LOCF approach and our model depending on prior beliefs on the imputation process. Our method can be regarded as a useful sensitivity analysis that can be used in the presence of concerns about the LOCF process. Copyright © 2014 JohnWiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Clinical trials are typically designed with an aim to reach sufficient power to test a hypothesis about relative effectiveness of two or more interventions. Their role in informing evidence-based decision-making demands, however, that they are considered in the context of the existing evidence. Consequently, their planning can be informed by characteristics of relevant systematic reviews and meta-analyses. In the presence of multiple competing interventions the evidence base has the form of a network of trials, which provides information not only about the required sample size but also about the interventions that should be compared in a future trial. In this paper we present a methodology to evaluate the impact of new studies, their information size, the comparisons involved, and the anticipated heterogeneity on the conditional power ( CP) of the updated network meta-analysis. The methods presented are an extension of the idea of CP initially suggested for a pairwise meta-analysis and we show how to estimate the required sample size using various combinations of direct and indirect evidence in future trials. We apply the methods to two previously published networks and we show that CP for a treatment comparison is dependent on the magnitude of heterogeneity and the ratio of direct to indirect information in existing and future trials for that comparison. Our methodology can help investigators calculate the required sample size under different assumptions about heterogeneity and make decisions about the number and design of future studies (set of treatments compared). [ABSTRACT FROM AUTHOR]

A multivariate meta-analysis of two or more correlated outcomes is expected to improve precision compared with a series of independent, univariate meta-analyses especially when there are studies reporting some but not all outcomes. Multivariate meta-analysis requires estimates of the within-study correlations, which are seldom available. Existing methods for analysing multiple outcomes simultaneously are limited to pairwise treatment comparisons. We propose a model for a joint, simultaneous synthesis of multiple dichotomous outcomes in a network of interventions and introduce a simple way to elicit expert opinion for the within-study correlations by utilizing a set of conditional probability parameters. We implement our multiple-outcomes network meta-analysis model within a Bayesian framework, which allows incorporation of expert information. As an example, we analyse two correlated dichotomous outcomes, response to the treatment and dropout rate, in a network of pharmacological interventions for acute mania. The produced estimates have narrower confidence intervals compared with the simple network meta-analysis. We conclude that the proposed model and the suggested prior elicitation method for correlations constitute a useful framework for performing network meta-analysis for multiple outcomes. [ABSTRACT FROM AUTHOR]

Missing outcome data are commonly encountered in randomized controlled trials and hence may need to be addressed in a meta-analysis of multiple trials. A common and simple approach to deal with missing data is to restrict analysis to individuals for whom the outcome was obtained (complete case analysis). However, estimated treatment effects from complete case analyses are potentially biased if informative missing data are ignored. We develop methods for estimating meta-analytic summary treatment effects for continuous outcomes in the presence of missing data for some of the individuals within the trials. We build on a method previously developed for binary outcomes, which quantifies the degree of departure from a missing at random assumption via the informative missingness odds ratio. Our new model quantifies the degree of departure from missing at random using either an informative missingness difference of means or an informative missingness ratio of means, both of which relate the mean value of the missing outcome data to that of the observed data. We propose estimating the treatment effects, adjusted for informative missingness, and their standard errors by a Taylor series approximation and by a Monte Carlo method. We apply the methodology to examples of both pairwise and network meta-analysis with multi-arm trials. [ABSTRACT FROM AUTHOR]

A problem that is frequently encountered during the systematic review process is when studies that meet the inclusion criteria do not provide the appropriate numerical estimates to include in a meta-analysis. For dichotomous outcomes, a method has been suggested by Di Pietrantonj for reconstructing the 2 × 2 table when the Odds Ratio ( OR), the Standard Error ( SE( lnOR)) and the sample sizes are provided. The method produces two possible 2 × 2 tables; and to select the correct one, the Control Group Risk ( CGR) is used. As CGR is typically unknown and only rounded figures of the OR and SE( lnOR) are provided, the accuracy of the reconstruction method varies. In this paper, we evaluate the performance of the method using simulated and empirical data. Small studies with large OR and CGR away from 50% are reconstructed satisfactorily, and the use of SE( lnOR) rounded to the third decimal rather than the second one improves the performance of the method. However, when CGR is unknown, its estimation from other studies is problematic as it exhibits high heterogeneity. Inclusion of an incorrectly reconstructed table in the meta-analysis may result in different summary effects. Reviewers that consider applying the method should be cautious about its impact in the meta-analysis. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

For a network meta-analysis, an interlinked network of nodes representing competing treatments is needed. It is often challenging to define the nodes as these typically refer to similar but rarely identical interventions. The objectives of this paper are as follows: (i) to present a series of network meta-analysis models that account for variation in the definition of the nodes and (ii) to exemplify the models where variation in the treatment definitions relates to the dose. Starting from the model that assumes each node has a 'fixed' definition, we gradually introduce terms to explain variability by assuming that each node has several subnodes that relate to different doses. The effects of subnodes are considered monotonic, linked with a 'random walk', random but exchangeable, or have a linear pattern around the treatment mean effect. Each model can be combined with different assumptions for the consistency of effects and might impact on the ranking of the treatments. Goodness of fit, heterogeneity and inconsistency were assessed. The models are illustrated in a star network for the effectiveness of fluoride toothpaste and in a full network comparing agents for multiple sclerosis. The fit and parsimony measures indicate that in the fluoride network the impact of the dose subnodes is important whereas in the multiple sclerosis network the model without subnodes is the most appropriate. The proposed approach can be a useful exploratory tool to explain sources of heterogeneity and inconsistency when there is doubt whether similar interventions should be grouped under the same node. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Lipids are a broad group of naturally occurring molecules, which includes fats, oils, waxes, sterols, terpenes, fat-soluble vitamins, monoglycerides, diglycerides, phospholipids and others. Lipids have been widely used in human history and they are often present in archaeological finds. In particular, in the field of medicine and cosmetics, lipids have been employed as base in the preparation of unguents, salves and balms. The characterization of these products and their degradation products induced by ageing is important for an understanding of the ancient pharmaceutical techniques and to assess the state of conservation. 1H-NMR and, for the first time, 31P-NMR spectroscopy have been applied to the characterization of such compounds. By derivatization of the samples with 2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane, it is possible to recognize, quantify and evaluate the degradation degree of lipids in archaeological unguent samples. With this new approach, lipids have been recognized in 17th-century archaeological ointments from the Aboca Museum. Moreover, severe hydrolysis and oxidation markers have been detected. [ABSTRACT FROM AUTHOR]

The ever increasing number of alternative treatment options and the plethora of clinical trials have put systematic reviews and meta-analysis under a new perspective by emphasizing the need to make inferences about competing treatments for the same condition. The statistical component in reviews that compare multiple interventions, network meta-analysis, is the next generation evidence synthesis toolkit which, when properly applied, can serve decision-making better than the established pairwise meta-analysis. The criticism and enthusiasm for network meta-analysis echo those that greeted the advent of simple meta-analysis. The main criticism is associated with the difficulty in evaluating the assumption underlying the statistical synthesis of direct and indirect evidence. In the present article, the assumption of the network meta-analysis are presented using various formulations, the statistical and nonstatistical methodological considerations are elucidated, and the progress achieved in this field is summarized. Throughout, focus is put on highlighting the analogy between the concerns and difficulties that the scientific community had some time ago when advancing from individual trials to their quantitative synthesis via meta-analysis and those currently expressed about the transition from head-to-head meta-analyses to network meta-analysis. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Suggested methods for exploring the presence of small-study effects in a meta-analysis and the possibility of publication bias are associated with important limitations. When a meta-analysis comprises only a few studies, funnel plots are difficult to interpret, and regression-based approaches to test and account for small-study effects have low power. Assuming that the cause of funnel plot asymmetry is likely to affect an entire research field rather than only a particular comparison of interventions, we suggest that network meta-regression is employed to account for small-study effects in a set of related meta-analyses. We present several possible models for the direction and distribution of small-study effects and we describe the methods by re-analysing two published networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multiple-treatments meta-analyses are increasingly used to evaluate the relative effectiveness of several competing regimens. In some fields which evolve with the continuous introduction of new agents over time, it is possible that in trials comparing older with newer regimens the effectiveness of the latter is exaggerated. Optimism bias, conflicts of interest and other forces may be responsible for this exaggeration, but its magnitude and impact, if any, needs to be formally assessed in each case. Whereas such novelty bias is not identifiable in a pair-wise meta-analysis, it is possible to explore it in a network of trials involving several treatments. To evaluate the hypothesis of novel agent effects and adjust for them, we developed a multiple-treatments meta-regression model fitted within a Bayesian framework. When there are several multiple-treatments meta-analyses for diverse conditions within the same field/specialty with similar agents involved, one may consider either different novel agent effects in each meta-analysis or may consider the effects to be exchangeable across the different conditions and outcomes. As an application, we evaluate the impact of modelling and adjusting for novel agent effects for chemotherapy and other non-hormonal systemic treatments for three malignancies. We present the results and the impact of different model assumptions to the relative ranking of the various regimens in each network. We established that multiple-treatments meta-regression is a good method for examining whether novel agent effects are present and estimation of their magnitude in the three worked examples suggests an exaggeration of the hazard ratio by 6 per cent (2-11 per cent). Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We describe how an appropriate interpretation of the Q-test depends on its power to detect a given typical amount of between-study variance (τ) as well as prior beliefs on heterogeneity. We illustrate these concepts in an evaluation of 1011 meta-analyses of clinical trials with ⩾4 studies and binary outcomes. These concepts can be seen as an application of the Bayes theorem. Across the 1011 meta-analyses, power to detect typical heterogeneity was low in most situations. Thus, usually a non-significant Q test did not change perceptibly prior convictions on heterogeneity. Conversely, significant results for the Q test typically augmented considerably the probability of heterogeneity. The posterior probability of heterogeneity depends on what τ we want to detect. With the same approach, one may also estimate the posterior probability for the presence of heterogeneity that is large enough to annul statistically significant summary effects; that is half the average within-study variance of the combined studies; and that is able to change the summary effect estimate of the meta-analysis by 20%. The discussed analyses are exploratory, and may depend heavily on prior assumptions when power for the Q-test is low. Statistical heterogeneity in meta-analyses should be cautiously interpreted considering the power to detect a specific τ and prior assumptions about the presence of heterogeneity. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Eur J Clin Invest 2010; 40 (2): 172–182 Background Most clinical trials on medical interventions are sponsored by the industry. The choice of comparators shapes the accumulated evidence. We aimed to assess how often major companies sponsor trials that involve only their own products. Methods Studies were identified by searching ClinicalTrials.gov for trials registered in 2006. We focused on randomized trials involving the 15 companies that had sponsored the largest number of registered trials in ClinicalTrials.gov in that period. Results Overall, 577 randomized trials were eligible for analysis and 82% had a single industry sponsor [89% (166/187) of the placebo-control trials, 87% (91/105) of trials comparing different doses or ways of administration of the same intervention, and 78% (221/285) of other active control trials]. The compared intervention(s) belonged to a single company in 67% of the trials (89%, 81% and 47% in the three categories respectively). All 15 companies strongly preferred to run trials where they were the only industry sponsor or even the only owner of the assessed interventions. Co-sponsorship typically reflected co-ownership of the same intervention by both companies. Head-to-head comparison of different active interventions developed by different companies occurred in only 18 trials with two or more industry sponsors. Conclusions Each company generates a clinical research agenda that is strongly focused on its own products, while comparisons involving different interventions from different companies are uncommon. This diminishes the ability to understand the relative merits of different interventions for the same condition. [ABSTRACT FROM AUTHOR]