Clinical characteristics of heart failure with reduced ejection fraction patients with rare pathogenic variants in dilated cardiomyopathy-associated genes: A subgroup analysis of the PARADIGM-HF trial.

AIMS: To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status. METHODS AND RESULTS: This was a post hoc analysis of the Phase 3 PARADIGM-HF trial. Forty-four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss-of-function (pLoF) variants, which were prioritized using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM-associated gene tiers. Clinical features were compared between carriers and non-carriers. Of the 1412 HFrEF participants with whole-exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier-1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier-1 variants only and 12.6% (29/229) had tier-1, -2 or -3 variants. Compared to non-carriers, tier-1 carriers were younger (4 years; adjusted p-value [padj ] = 4 × 10-3 ), leaner (27.8 kg/m2 vs. 29.4 kg/m2 ; padj = 3.2 × 10-3 ), had lower ejection fraction (27.3% vs. 29.8%; padj = 5.8 × 10-3 ), and less likely to have ischaemic aetiology (37.3% vs. 67.4%; padj = 4 × 10-4 ). CONCLUSION: Deleterious pLoF variants in genes with definitive/strong association with DCM were identified in ∼5% of HFrEF patients from a PARADIGM-HF trial subset, who were younger, had lower ejection fraction and were less likely to have had an ischaemic aetiology.

Innovations in Pediatric Therapeutics Development: Principles for the Use of Bridging Biomarkers in Pediatric Extrapolation.

Even with recent substantive improvements in health care in pediatric populations, considerable need remains for additional safe and effective interventions for the prevention and treatment of diseases in children. The approval of prescription drugs and biological products for use in pediatric settings, as in adults, requires demonstration of substantial evidence of effectiveness and favorable benefit-to-risk. For diseases primarily affecting children, such evidence predominantly would be obtained in the pediatric setting. However, for conditions affecting both adults and children, pediatric extrapolation uses scientific evidence in adults to enable more efficiently obtaining a reliable evaluation of an intervention's effects in pediatric populations. Bridging biomarkers potentially have an integral role in pediatric extrapolation. In a setting where an intervention reliably has been established to be safe and effective in adults, and where there is substantive evidence that disease processes in pediatric and adult settings are biologically similar, a 'bridging biomarker' should satisfy three additional criteria: effects on the bridging biomarker should capture effects on the principal causal pathway through which the disease process meaningfully influences 'feels, functions, survives' measures; secondly, the experimental intervention should not have important unintended effects on 'feels, functions, survives' measures not captured by the bridging biomarker; and thirdly, in statistical analyses in adults, the intervention's net effect on 'feels, functions, survives' measures should be consistent with what would be predicted by its level of effect on the bridging biomarker. A validated bridging biomarker has considerable potential utility, since an intervention's efficacy could be extrapolated from adult to pediatric populations if evidence in children establishes the intervention not only to be safe but also to have substantive effects on that bridging biomarker. Proper use of bridging biomarkers could increase availability of reliably evaluated therapies approved for use in pediatric settings, enabling children and their caregivers to make informed choices about health care.

Rethinking clinical study data: why we should respect analysis results as data.

The development and approval of new treatments generates large volumes of results, such as summaries of efficacy and safety. However, it is commonly overlooked that analyzing clinical study data also produces data in the form of results. For example, descriptive statistics and model predictions are data. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. Results end up stored as "data products" such as PDF documents that are not machine readable or amenable to future analyses. We propose a solution to "calculate once, use many times" by combining analysis results standards with a common data model. This analysis results data model re-frames the target of analyses from static representations of the results (e.g., tables and figures) to a data model with applications in various contexts, including knowledge discovery. Further, we provide a working proof of concept detailing how to approach standardization and construct a schema to store and query analysis results.

The Predictive Individual Effect for Survival Data.

BACKGROUND: The call for patient-focused drug development is loud and clear, as expressed in the twenty-first Century Cures Act and in recent guidelines and initiatives of regulatory agencies. Among the factors contributing to modernized drug development and improved health-care activities are easily interpretable measures of clinical benefit. In addition, special care is needed for cancer trials with time-to-event endpoints if the treatment effect is not constant over time. OBJECTIVE: To quantify the potential clinical survival benefit for a new patient, would he/she be treated with the test or control treatment. METHODS: We propose the predictive individual effect which is a patient-centric and tangible measure of clinical benefit under a wide variety of scenarios. It can be obtained by standard predictive calculations under a rank preservation assumption that has been used previously in trials with treatment switching. RESULTS: We discuss four recent Oncology trials that cover situations with proportional as well as non-proportional hazards (delayed treatment effect or crossing of survival curves). It is shown that the predictive individual effect offers valuable insights beyond p-values, estimates of hazard ratios or differences in median survival. CONCLUSION: Compared to standard statistical measures, the predictive individual effect is a direct, easily interpretable measure of clinical benefit. It facilitates communication among clinicians, patients, and other parties and should therefore be considered in addition to standard statistical results.

Predicting drug approvals: The Novartis data science and artificial intelligence challenge.

We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model-areas under the curve of 0.88 and 0.84 versus 0.78, respectively-through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.

A robust Bayesian meta-analytic approach to incorporate animal data into phase I oncology trials.

Before a first-in-man trial is conducted, preclinical studies are performed in animals to help characterise the safety profile of the new medicine. We propose a robust Bayesian hierarchical model to synthesise animal and human toxicity data, using scaling factors to translate doses administered to different animal species onto an equivalent human scale. After scaling doses, the parameters of dose-toxicity models intrinsic to different animal species can be interpreted on a common scale. A prior distribution is specified for each translation factor to capture uncertainty about differences between toxicity of the drug in animals and humans. Information from animals can then be leveraged to learn about the relationship between dose and risk of toxicity in a new phase I trial in humans. The model allows human dose-toxicity parameters to be exchangeable with the study-specific parameters of animal species studied so far or non-exchangeable with any of them. This leads to robust inferences, enabling the model to give greatest weight to the animal data with parameters most consistent with human parameters or discount all animal data in the case of non-exchangeability. The proposed model is illustrated using a case study and simulations. Numerical results suggest that our proposal improves the precision of estimates of the toxicity rates when animal and human data are consistent, while it discounts animal data in cases of inconsistency.

Model averaging for robust extrapolation in evidence synthesis.

Extrapolation from a source to a target, eg, from adults to children, is a promising approach to utilize external information when data are sparse. In the context of meta-analyses, one is commonly faced with a small number of studies, whereas potentially relevant additional information may also be available. Here, we describe a simple extrapolation strategy using heavy-tailed mixture priors for effect estimation in meta-analysis, which effectively results in a model-averaging technique. The described method is robust in the sense that a potential prior-data conflict, ie, a discrepancy between source and target data, is explicitly anticipated. The aim of this paper is to develop a solution for this particular application to showcase the ease of implementation by providing R code, and to demonstrate the robustness of the general approach in simulations.

Effectiveness of non-steroidal anti-inflammatory drugs for the treatment of pain in knee and hip osteoarthritis: a network meta-analysis.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are the backbone of osteoarthritis pain management. We aimed to assess the effectiveness of different preparations and doses of NSAIDs on osteoarthritis pain in a network meta-analysis. METHODS: For this network meta-analysis, we considered randomised trials comparing any of the following interventions: NSAIDs, paracetamol, or placebo, for the treatment of osteoarthritis pain. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) and the reference lists of relevant articles for trials published between Jan 1, 1980, and Feb 24, 2015, with at least 100 patients per group. The prespecified primary and secondary outcomes were pain and physical function, and were extracted in duplicate for up to seven timepoints after the start of treatment. We used an extension of multivariable Bayesian random effects models for mixed multiple treatment comparisons with a random effect at the level of trials. For the primary analysis, a random walk of first order was used to account for multiple follow-up outcome data within a trial. Preparations that used different total daily dose were considered separately in the analysis. To assess a potential dose-response relation, we used preparation-specific covariates assuming linearity on log relative dose. FINDINGS: We identified 8973 manuscripts from our search, of which 76 randomised trials with a total of 58 451 patients were included in this analysis. 23 nodes concerning seven different NSAIDs or paracetamol with specific daily dose of administration or placebo were considered. All preparations, irrespective of dose, improved point estimates of pain symptoms when compared with placebo. For six interventions (diclofenac 150 mg/day, etoricoxib 30 mg/day, 60 mg/day, and 90 mg/day, and rofecoxib 25 mg/day and 50 mg/day), the probability that the difference to placebo is at or below a prespecified minimum clinically important effect for pain reduction (effect size [ES] -0·37) was at least 95%. Among maximally approved daily doses, diclofenac 150 mg/day (ES -0·57, 95% credibility interval [CrI] -0·69 to -0·45) and etoricoxib 60 mg/day (ES -0·58, -0·74 to -0·43) had the highest probability to be the best intervention, both with 100% probability to reach the minimum clinically important difference. Treatment effects increased as drug dose increased, but corresponding tests for a linear dose effect were significant only for naproxen (p=0·034). We found no evidence that treatment effects varied over the duration of treatment. Model fit was good, and between-trial heterogeneity and inconsistency were low in all analyses. All trials were deemed to have a low risk of bias for blinding of patients. Effect estimates did not change in sensitivity analyses with two additional statistical models and accounting for methodological quality criteria in meta-regression analysis. INTERPRETATION: On the basis of the available data, we see no role for single-agent paracetamol for the treatment of patients with osteoarthritis irrespective of dose. We provide sound evidence that diclofenac 150 mg/day is the most effective NSAID available at present, in terms of improving both pain and function. Nevertheless, in view of the safety profile of these drugs, physicians need to consider our results together with all known safety information when selecting the preparation and dose for individual patients. FUNDING: Swiss National Science Foundation (grant number 405340-104762) and Arco Foundation, Switzerland.

Using phase II data for the analysis of phase III studies: An application in rare diseases.

BACKGROUND: Clinical research and drug development in orphan diseases are challenging, since large-scale randomized studies are difficult to conduct. Formally synthesizing the evidence is therefore of great value, yet this is rarely done in the drug-approval process. Phase III designs that make better use of phase II data can facilitate drug development in orphan diseases. METHODS: A Bayesian meta-analytic approach is used to inform the phase III study with phase II data. It is particularly attractive, since uncertainty of between-trial heterogeneity can be dealt with probabilistically, which is critical if the number of studies is small. Furthermore, it allows quantifying and discounting the phase II data through the predictive distribution relevant for phase III. A phase III design is proposed which uses the phase II data and considers approval based on a phase III interim analysis. The design is illustrated with a non-inferiority case study from a Food and Drug Administration approval in herpetic keratitis (an orphan disease). Design operating characteristics are compared to those of a traditional design, which ignores the phase II data. RESULTS: An analysis of the phase II data reveals good but insufficient evidence for non-inferiority, highlighting the need for a phase III study. For the phase III study supported by phase II data, the interim analysis is based on half of the patients. For this design, the meta-analytic interim results are conclusive and would justify approval. In contrast, based on the phase III data only, interim results are inconclusive and require further evidence. CONCLUSION: To accelerate drug development for orphan diseases, innovative study designs and appropriate methodology are needed. Taking advantage of randomized phase II data when analyzing phase III studies looks promising because the evidence from phase II supports informed decision-making. The implementation of the Bayesian design is straightforward with public software such as R.

Renin angiotensin system inhibitors for patients with stable coronary artery disease without heart failure: systematic review and meta-analysis of randomized trials.

OBJECTIVE:  To critically evaluate the efficacy of renin angiotensin system inhibitors (RASi) in patients with coronary artery disease without heart failure, compared with active controls or placebo. DESIGN:  Meta-analysis of randomized trials. DATA SOURCES:  PubMed, EMBASE, and CENTRAL databases until 1 May 2016. ELIGIBILITY CRITERIA FOR SELECTING STUDIES:  Randomized trials of RASi versus placebo or active controls in patients with stable coronary artery disease without heart failure (defined as left ventricular ejection fraction ≥40% or without clinical heart failure). Each trial had to enroll at least 100 patients with coronary artery disease without heart failure, with at least one year's follow-up. Studies were excluded if they were redacted or compared use of angiotensin converting enzyme inhibitors with angiotensin receptor blockers. Outcomes were death, cardiovascular death, myocardial infarction, angina, stroke, heart failure, revascularization, incident diabetes, and drug withdrawal due to adverse effects. RESULTS:  24 trials with 198 275 patient years of follow-up were included. RASi reduced the risk of all cause mortality (rate ratio 0.84, 95% confidence interval 0.72 to 0.98), cardiovascular mortality (0.74, 0.59 to 0.94), myocardial infarction (0.82, 0.76 to 0.88), stroke (0.79, 0.70 to 0.89), angina, heart failure, and revascularization when compared with placebo but not when compared with active controls (all cause mortality, 1.05, 0.94 to 1.17; Pinteraction=0.006; cardiovascular mortality, 1.08, 0.93 to 1.25, Pinteraction<0.001; myocardial infarction, 0.99, 0.87 to 1.12, Pinteraction=0.01; stroke, 1.10, 0.93 to 1.31; Pinteraction=0.002). Bayesian meta-regression analysis showed that the effect of RASi when compared with placebo on all cause mortality and cardiovascular mortality was dependent on the control event rate, such that RASi was only beneficial in trials with high control event rates (>14.10 deaths and >7.65 cardiovascular deaths per 1000 patient years) but not in those with low control event rates. CONCLUSIONS:  In patients with stable coronary artery disease without heart failure, RASi reduced cardiovascular events and death only when compared with placebo but not when compared with active controls. Even among placebo controlled trials in this study, the benefit of RASi was mainly seen in trials with higher control event rates but not in those with lower control event rates. Evidence does not support a preferred status of RASi over other active controls.

Meta-analysis of few small studies in orphan diseases.

Meta-analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random-effects meta-analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between-trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp-Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between-trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half-normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes.

Designing and analysing clinical trials in mental health: an evidence synthesis approach.

OBJECTIVE: When planning a clinical study, evidence on the treatment effect is often available from previous studies. However, this evidence is mostly ignored for the analysis of the new study. This is unfortunate, since using it could lead to a smaller study without compromising power. We describe a design that addresses this issue. METHODS: We use a Bayesian meta-analytic model to incorporate the available evidence in the analysis of the new study. The shrinkage estimate for the new study integrates the evidence from the other studies. At the planning phase of the study, it allows a statistically justified reduction of the sample size. RESULTS: The design is illustrated using data from an Food and Drug Administration (FDA) review of lurasidone for the treatment of schizophrenia. Three studies inform the meta-analysis before the new study is conducted. Results from an additional phase III study, which were not available at the time of the FDA review, are then used for the actual analysis. CONCLUSIONS: In the presence of reliable and relevant evidence, the design offers a way to conduct a smaller study without compromising power. It therefore fills a gap between the assessment of evidence and its actual use in the design and analysis of studies.

RETRACTED: Effectiveness of non-steroidal anti-inflammatory drugs for the treatment of pain in knee and hip osteoarthritis: a network meta-analysis.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are the backbone of osteoarthritis pain management. We aimed to assess the effectiveness of different preparations and doses of NSAIDs on osteoarthritis pain in a network meta-analysis. METHODS: For this network meta-analysis, we considered randomised trials comparing any of the following interventions: NSAIDs, paracetamol, or placebo, for the treatment of osteoarthritis pain. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) and the reference lists of relevant articles for trials published between Jan 1, 1980, and Feb 24, 2015, with at least 100 patients per group. The prespecified primary and secondary outcomes were pain and physical function, and were extracted in duplicate for up to seven timepoints after the start of treatment. We used an extension of multivariable Bayesian random effects models for mixed multiple treatment comparisons with a random effect at the level of trials. For the primary analysis, a random walk of first order was used to account for multiple follow-up outcome data within a trial. Preparations that used different total daily dose were considered separately in the analysis. To assess a potential dose-response relation, we used preparation-specific covariates assuming linearity on log relative dose. FINDINGS: We identified 8973 manuscripts from our search, of which 74 randomised trials with a total of 58,556 patients were included in this analysis. 23 nodes concerning seven different NSAIDs or paracetamol with specific daily dose of administration or placebo were considered. All preparations, irrespective of dose, improved point estimates of pain symptoms when compared with placebo. For six interventions (diclofenac 150 mg/day, etoricoxib 30 mg/day, 60 mg/day, and 90 mg/day, and rofecoxib 25 mg/day and 50 mg/day), the probability that the difference to placebo is at or below a prespecified minimum clinically important effect for pain reduction (effect size [ES] -0·37) was at least 95%. Among maximally approved daily doses, diclofenac 150 mg/day (ES -0·57, 95% credibility interval [CrI] -0·69 to -0·46) and etoricoxib 60 mg/day (ES -0·58, -0·73 to -0·43) had the highest probability to be the best intervention, both with 100% probability to reach the minimum clinically important difference. Treatment effects increased as drug dose increased, but corresponding tests for a linear dose effect were significant only for celecoxib (p=0·030), diclofenac (p=0·031), and naproxen (p=0·026). We found no evidence that treatment effects varied over the duration of treatment. Model fit was good, and between-trial heterogeneity and inconsistency were low in all analyses. All trials were deemed to have a low risk of bias for blinding of patients. Effect estimates did not change in sensitivity analyses with two additional statistical models and accounting for methodological quality criteria in meta-regression analysis. INTERPRETATION: On the basis of the available data, we see no role for single-agent paracetamol for the treatment of patients with osteoarthritis irrespective of dose. We provide sound evidence that diclofenac 150 mg/day is the most effective NSAID available at present, in terms of improving both pain and function. Nevertheless, in view of the safety profile of these drugs, physicians need to consider our results together with all known safety information when selecting the preparation and dose for individual patients. FUNDING: Swiss National Science Foundation (grant number 405340-104762) and Arco Foundation, Switzerland.

Robust exchangeability designs for early phase clinical trials with multiple strata.

Clinical trials with multiple strata are increasingly used in drug development. They may sometimes be the only option to study a new treatment, for example in small populations and rare diseases. In early phase trials, where data are often sparse, good statistical inference and subsequent decision-making can be challenging. Inferences from simple pooling or stratification are known to be inferior to hierarchical modeling methods, which build on exchangeable strata parameters and allow borrowing information across strata. However, the standard exchangeability (EX) assumption bears the risk of too much shrinkage and excessive borrowing for extreme strata. We propose the exchangeability-nonexchangeability (EXNEX) approach as a robust mixture extension of the standard EX approach. It allows each stratum-specific parameter to be exchangeable with other similar strata parameters or nonexchangeable with any of them. While EXNEX computations can be performed easily with standard Bayesian software, model specifications and prior distributions are more demanding and require a good understanding of the context. Two case studies from phases I and II (with three and four strata) show promising results for EXNEX. Data scenarios reveal tempered degrees of borrowing for extreme strata, and frequentist operating characteristics perform well for estimation (bias, mean-squared error) and testing (less type-I error inflation).

MEK162 for patients with advanced melanoma harbouring NRAS or Val600 BRAF mutations: a non-randomised, open-label phase 2 study.

BACKGROUND: Patients with melanoma harbouring Val600 BRAF mutations benefit from treatment with BRAF inhibitors. However, no targeted treatments exist for patients with BRAF wild-type tumours, including those with NRAS mutations. We aimed to assess the use of MEK162, a small-molecule MEK1/2 inhibitor, in patients with NRAS-mutated or Val600 BRAF-mutated advanced melanoma. METHODS: In our open-label, non-randomised, phase 2 study, we assigned patients with NRAS-mutated or BRAF-mutated advanced melanoma to one of three treatment arms on the basis of mutation status. Patients were enrolled at university hospitals or private cancer centres in Europe and the USA. The three arms were: twice-daily MEK162 45 mg for NRAS-mutated melanoma, twice-daily MEK162 45 mg for BRAF-mutated melanoma, and twice-daily MEK162 60 mg for BRAF-mutated melanoma. Previous treatment with BRAF inhibitors was permitted, but previous MEK inhibitor therapy was not allowed. The primary endpoint was the proportion of patients who had an objective response (ie, a complete response or confirmed partial response). We report data for the 45 mg groups. We assessed clinical activity in all patients who received at least one dose of MEK162 and in patients assessable for response (with two available CT scans). This study is registered with ClinicalTrials.gov, number NCT01320085, and is currently recruiting additional patients with NRAS mutations (based on a protocol amendment). FINDINGS: Between March 31, 2011, and Jan 17, 2012, we enrolled 71 patients who received at least one dose of MEK162 45 mg. By Feb 29, 2012 (data cutoff), median follow-up was 3·3 months (range 0·6-8·7; IQR 2·2-5·0). No patients had a complete response. Six (20%) of 30 patients with NRAS-mutated melanoma had a partial response (three confirmed) as did eight (20%) of 41 patients with BRAF-mutated melanoma (two confirmed). The most frequent adverse events were acneiform dermatitis (18 [60%] patients with NRAS -mutated melanoma and 15 [37%] patients with the BRAF-mutated melanoma), rash (six [20%] and 16 [39%]), peripheral oedema (ten [33%] and 14 [34%]), facial oedema (nine [30%] and seven [17%]), diarrhoea (eight [27%] and 15 [37%]), and creatine phosphokinase increases (11 [37%] and nine [22%]). Increased creatine phosphokinase was the most common grade 3-4 adverse event (seven [23%] and seven [17%]). Four patients had serious adverse events (two per arm), which included diarrhoea, dehydration, acneiform dermatitis, general physical deterioration, irregular heart rate, malaise, and small intestinal perforation. No deaths occurred from treatment-related causes. INTERPRETATION: To our knowledge, MEK162 is the first targeted therapy to show activity in patients with NRAS -mutated melanoma and might offer a new option for a cancer with few effective treatments. FUNDING: Novartis Pharmaceuticals.

The network meta-analytic-predictive approach to non-inferiority trials.

In non-inferiority clinical trials, a test treatment is compared to an active-control rather than to placebo. Such designs are considered when placebo is unethical or not feasible. The critical question is whether the test treatment would have been superior to placebo, had placebo been used in the non-inferiority trial. This question can only be addressed indirectly, based on information from relevant historical trials with data on active-control and placebo. The network meta-analytic-predictive approach to non-inferiority trials is based on a network meta-analysis of the data from the historical trials and the non-inferiority trial, and the prediction of the putative test vs. placebo effect in the non-inferiority trial. The approach extends previous work by incorporating between-trial variability for all relevant parameters and focusing on the parameters in the non-inferiority trial rather than on population means. Two prominent examples with binary outcomes are used to illustrate the approach.

Five-year clinical and angiographic outcomes of a randomized comparison of sirolimus-eluting and paclitaxel-eluting stents: results of the Sirolimus-Eluting Versus Paclitaxel-Eluting Stents for Coronary Revascularization LATE trial.

BACKGROUND: Long-term comparative data of first-generation drug-eluting stents are scarce. We investigated clinical and angiographic outcomes of sirolimus-eluting (SES) and paclitaxel-eluting stents (PES) at 5 years as part of the Sirolimus-Eluting Versus Paclitaxel-Eluting Stents for Coronary Revascularization (SIRTAX) LATE study. METHODS AND RESULTS: A total of 1012 patients were randomly assigned to SES or PES. Repeat angiography was completed in 444 of 1012 patients (43.8%) at 5 years. Major adverse cardiac events occurred in 19.7% of SES- and 21.4% of PES-treated patients (hazard ratio, 0.89; 95% confidence interval, 0.68 to 1.17; P=0.39) at 5 years. There were no differences between SES and PES in terms of cardiac death (5.8% versus 5.7%; P=0.35), myocardial infarction (6.6% versus 6.9%; P=0.51), and target lesion revascularization (13.1% versus 15.1%; P=0.29). Between 1 and 5 years, the annual rate of target lesion revascularization was 2.0% (95% confidence interval, 1.4% to 2.6%) for SES and 1.4% (95% confidence interval, 0.9% to 2.0%) for PES. Among patients undergoing paired angiography at 8 months and 5 years, delayed lumen loss amounted to 0.37 ± 0.73 mm for SES and 0.29 ± 0.59 mm for PES (P=0.32). The overall rate of definite stent thrombosis was 4.6% for SES and 4.1% for PES (P=0.74), and very late definite stent thrombosis occurred at an annual rate of 0.65% (95% confidence interval, 0.40% to 0.90%). CONCLUSIONS: Long-term follow-up of first-generation drug-eluting stents shows no significant differences in clinical and angiographic outcomes between SES and PES. The continuous increase in late lumen loss in conjunction with the ongoing risk of very late stent thrombosis suggests that vascular healing remains incomplete up to 5 years after implantation of first-generation drug-eluting stents.

How valuable are multiple treatment comparison methods in evidence-based health-care evaluation?

OBJECTIVES: To compare the use of pair-wise meta-analysis methods to multiple treatment comparison (MTC) methods for evidence-based health-care evaluation to estimate the effectiveness and cost-effectiveness of alternative health-care interventions based on the available evidence. METHODS: Pair-wise meta-analysis and more complex evidence syntheses, incorporating an MTC component, are applied to three examples: 1) clinical effectiveness of interventions for preventing strokes in people with atrial fibrillation; 2) clinical and cost-effectiveness of using drug-eluting stents in percutaneous coronary intervention in patients with coronary artery disease; and 3) clinical and cost-effectiveness of using neuraminidase inhibitors in the treatment of influenza. We compare the two synthesis approaches with respect to the assumptions made, empirical estimates produced, and conclusions drawn. RESULTS: The difference between point estimates of effectiveness produced by the pair-wise and MTC approaches was generally unpredictable-sometimes agreeing closely whereas in other instances differing considerably. In all three examples, the MTC approach allowed the inclusion of randomized controlled trial evidence ignored in the pair-wise meta-analysis approach. This generally increased the precision of the effectiveness estimates from the MTC model. CONCLUSIONS: The MTC approach to synthesis allows the evidence base on clinical effectiveness to be treated as a coherent whole, include more data, and sometimes relax the assumptions made in the pair-wise approaches. However, MTC models are necessarily more complex than those developed for pair-wise meta-analysis and thus could be seen as less transparent. Therefore, it is important that model details and the assumptions made are carefully reported alongside the results.

Cardiovascular safety of non-steroidal anti-inflammatory drugs: network meta-analysis.

OBJECTIVE: To analyse the available evidence on cardiovascular safety of non-steroidal anti-inflammatory drugs. DESIGN: Network meta-analysis. DATA SOURCES: Bibliographic databases, conference proceedings, study registers, the Food and Drug Administration website, reference lists of relevant articles, and reports citing relevant articles through the Science Citation Index (last update July 2009). Manufacturers of celecoxib and lumiracoxib provided additional data. STUDY SELECTION: All large scale randomised controlled trials comparing any non-steroidal anti-inflammatory drug with other non-steroidal anti-inflammatory drugs or placebo. Two investigators independently assessed eligibility. DATA EXTRACTION: The primary outcome was myocardial infarction. Secondary outcomes included stroke, death from cardiovascular disease, and death from any cause. Two investigators independently extracted data. DATA SYNTHESIS: 31 trials in 116 429 patients with more than 115 000 patient years of follow-up were included. Patients were allocated to naproxen, ibuprofen, diclofenac, celecoxib, etoricoxib, rofecoxib, lumiracoxib, or placebo. Compared with placebo, rofecoxib was associated with the highest risk of myocardial infarction (rate ratio 2.12, 95% credibility interval 1.26 to 3.56), followed by lumiracoxib (2.00, 0.71 to 6.21). Ibuprofen was associated with the highest risk of stroke (3.36, 1.00 to 11.6), followed by diclofenac (2.86, 1.09 to 8.36). Etoricoxib (4.07, 1.23 to 15.7) and diclofenac (3.98, 1.48 to 12.7) were associated with the highest risk of cardiovascular death. CONCLUSIONS: Although uncertainty remains, little evidence exists to suggest that any of the investigated drugs are safe in cardiovascular terms. Naproxen seemed least harmful. Cardiovascular risk needs to be taken into account when prescribing any non-steroidal anti-inflammatory drug.