Efficacy and safety of diclofenac in osteoarthritis: Results of a network meta-analysis of unpublished legacy studies.

Background And Aim: Diclofenac is widely prescribed for the treatment of pain. Several network meta-analyses (NMA), largely of published trials have evaluated the efficacy, tolerability, and safety of non-steroidal anti-inflammatory drugs (NSAIDs). The present NMA extends these analyses to unpublished older (legacy) diclofenac trials.

High correlation of VAS pain scores after 2 and 6 weeks of treatment with VAS pain scores at 12 weeks in randomised controlled trials in rheumatoid arthritis and osteoarthritis: meta-analysis and implications.

Background: Researchers in clinical trials in rheumatoid arthritis (RA) and osteoarthritis (OA) often measure pain levels with a visual analogue scale (VAS). Of interest to clinical practice and future clinical trial design are associations of change from baseline (CFB) between time points with predictive ability of earlier response for long-term treatment benefit. We assessed the association and predictive ability of CFB in VAS pain between 2, 6 and 12 weeks in randomised controlled trials (RCTs) of non-steroidal anti-inflammatory drugs (NSAIDs).

Review and comparison of methodologies for indirect comparison of clinical trial results: an illustration with ranibizumab and aflibercept.

Aim: To review and compare methods for indirect comparison of aflibercept and ranibizumab in patients with diabetic macular edema.

Methods: Post-stratification, inverse probability weighting based on simulated data, weight optimization, and regression model techniques were used to compare pooled individual patient-level data from the RESTORE and RESPOND (ranibizumab 0.5 mg as needed after 3 initial monthly doses) studies with summary-level data from the VIVID and VISTA (aflibercept 2.

Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

Background: Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct comparisons of two or more treatments for the management of disease by combining evidence from multiple studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include single-arm, before-and-after, observational studies to complete these disconnected networks.

Relative benefit-risk comparing diclofenac to other traditional non-steroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors in patients with osteoarthritis or rheumatoid arthritis: a network meta-analysis.

Introduction: There is argument over the benefits and risks of drugs for treating chronic musculoskeletal pain. This study compared the efficacy, safety, and tolerability of diclofenac, ibuprofen, naproxen, celecoxib, and etoricoxib for patients with pain caused by osteoarthritis (OA) or rheumatoid arthritis (RA).

Methods: A systematic literature review used Medline and EMBASE to identify randomised controlled trials.

A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment.

Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefit-risk assessment to formalize trade-offs between benefits and risks, providing transparency to the assessment process. There is however no well-established method for propagating uncertainty of treatment effects data through such models to provide a sense of the variability of the benefit-risk balance. Here, we present a Bayesian statistical method that directly models the outcomes observed in randomized placebo-controlled trials and uses this to infer indirect comparisons between competing active treatments.

A case study using the PrOACT-URL and BRAT frameworks for structured benefit risk assessment.

While benefit-risk assessment is a key component of the drug development and maintenance process, it is often described in a narrative. In contrast, structured benefit-risk assessment builds on established ideas from decision analysis and comprises a qualitative framework and quantitative methodology. We compare two such frameworks, applying multi-criteria decision-analysis (MCDA) within the PrOACT-URL framework and weighted net clinical benefit (wNCB), within the BRAT framework.

Indirect comparisons of ranibizumab and dexamethasone in macular oedema secondary to retinal vein occlusion.

Background: Two treatments, ranibizumab and dexamethasone implant, for visual impairment due to macular oedema (ME) secondary to retinal vein occlusion (RVO) have recently been studied in clinical trials. There have been no head to head comparisons of the two treatments, and improvement measured as gain in Best Corrected Visual Acuity (BCVA) was reported using different outcomes thresholds between trials. To overcome these limitations, and inform an economic model, we developed a combination of a multinomial model and an indirect Bayesian comparison model for multinomial outcomes.

No evidence of disease activity: indirect comparisons of oral therapies for the treatment of relapsing-remitting multiple sclerosis.

Introduction: No head-to-head trials have compared the efficacy of the oral therapies, fingolimod, dimethyl fumarate and teriflunomide, in multiple sclerosis. Statistical modeling approaches, which control for differences in patient characteristics, can improve indirect comparisons of the efficacy of these therapies.

Methods: No evidence of disease activity (NEDA) was evaluated as the proportion of patients free from relapses and 3-month confirmed disability progression (clinical composite), free from gadolinium-enhancing T1 lesions and new or newly enlarged T2 lesions (magnetic resonance imaging composite), or free from all disease measures (overall composite).

Benefit-risk assessment in a post-market setting: a case study integrating real-life experience into benefit-risk methodology.

Purpose: Difficulties may be encountered when undertaking a benefit-risk assessment for an older product with well-established use but with a benefit-risk balance that may have changed over time. This case study investigates this specific situation by applying a formal benefit-risk framework to assess the benefit-risk balance of warfarin for primary prevention of patients with atrial fibrillation.

Methods: We used the qualitative framework BRAT as the starting point of the benefit-risk analysis, bringing together the relevant available evidence.

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data.

Reducing blood culture contamination rates: a systematic approach to improving quality of care.

Contaminated blood cultures can have a deleterious effect on patient care; they may lead to longer hospital stays, unnecessary antibiotic therapy, needless removal of central lines, and redundant laboratory testing. A multidisciplinary quality improvement team from a western US health care system used an evidence-based process to define a system for obtaining blood culture specimens that subsequently decreased contamination rates from 3.7% to 1.

Methods for covariate adjustment in cost-effectiveness analysis that use cluster randomised trials.

Statistical methods have been developed for cost-effectiveness analyses of cluster randomised trials (CRTs) where baseline covariates are balanced. However, CRTs may show systematic differences in individual and cluster-level covariates between the treatment groups. This paper presents three methods to adjust for imbalances in observed covariates: seemingly unrelated regression with a robust standard error, a 'two-stage' bootstrap approach combined with seemingly unrelated regression and multilevel models.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

Aim: Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs.

Statistical methods for cost-effectiveness analyses that use data from cluster randomized trials: a systematic review and checklist for critical appraisal.

Introduction: The best data for cost-effectiveness analyses (CEAs) of group-level interventions often come from cluster randomized trials (CRTs), where randomization is by cluster (e.g., the hospital attended), not by individual.

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs.

Cost-effectiveness of disease-modifying therapies in the management of multiple sclerosis for the Medicare population.

Objective: To evaluate the cost-effectiveness of disease-modifying therapies (DMTs) for the management of multiple sclerosis (MS) compared to best supportive care in the United States.

Methods: Cost-effectiveness analysis was undertaken using a state transition model of disease natural history and the impact of DMTs for the representative Medicare beneficiary with MS. Costs and outcomes were evaluated from the health-care payer perspective using a 50-year time horizon.

Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared.

Cost-effectiveness analyses (CEA) alongside randomised controlled trials commonly estimate incremental net benefits (INB), with 95% confidence intervals, and compute cost-effectiveness acceptability curves and confidence ellipses. Two alternative non-parametric methods for estimating INB are to apply the central limit theorem (CLT) or to use the non-parametric bootstrap method, although it is unclear which method is preferable. This paper describes the statistical rationale underlying each of these methods and illustrates their application with a trial-based CEA.

The Rheumatoid Arthritis Drug Development Model: a case study in Bayesian clinical trial simulation.

The development of a new drug is a major undertaking and it is important to consider carefully the key decisions in the development process. Decisions are made in the presence of uncertainty and outcomes such as the probability of successful drug registration depend on the clinical development programmme.The Rheumatoid Arthritis Drug Development Model was developed to support key decisions for drugs in development for the treatment of rheumatoid arthritis.

Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis.

A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months.

Biologic drugs for rheumatoid arthritis in the Medicare program: a cost-effectiveness analysis.

Objective: Since the introduction of the Medicare Prescription Drug Improvement and Modernization Act and its associated demonstration project, coverage of selected biologic drugs has been expanded for Medicare beneficiaries. For rheumatoid arthritis, coverage was extended to etanercept, adalimumab, and anakinra in addition to the previously covered infliximab. We undertook to develop a model to compare the costs and quality-adjusted life years (QALYs) generated by each of the 4 biologic agents.

The distribution of the cost of multiple sclerosis in the UK: how do costs vary by illness severity?

Objectives: To derive robust estimates for the cost of multiple sclerosis (MS) based on a variety of cost factors across a number of different cost perspectives. This is essential to perform credible pharmacoeconomic evaluations of alternative MS therapies.

Methods: Here we present a detailed analysis of previously published MS cost data for the UK to which we fit a seemingly unrelated regression.

The effect of disease, functional status, and relapses on the utility of people with multiple sclerosis in the UK.

Objectives: Because published utility estimates in multiple sclerosis (MS) are concentrated in people with moderate to severe disease severity and focus on specific types of MS, we conducted a cross-sectional study of people with MS to estimate the utility associated with disease, functional status as measured by the Adapted Patient Determined Disease Steps (APDDS) Scale, and relapse to enhance knowledge of the association of these factors and utility.

Methods: The study was conducted by a postal questionnaire sent to 12,968 people in a database managed by a UK charity (the MS Trust). Utility was assessed using the EQ-5D multiattribute utility scale.

Multilevel models for estimating incremental net benefits in multinational studies.

Multilevel models (MLMs) have been recommended for estimating incremental net benefits (INBs) in multicentre cost-effectiveness analysis (CEA). However, these models have assumed that the INBs are exchangeable and that there is a common variance across all centres. This paper examines the plausibility of these assumptions by comparing various MLMs for estimating the mean INB in a multinational CEA.