Efficacy and safety of diclofenac in osteoarthritis: Results of a network meta-analysis of unpublished legacy studies.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 The present NMA extends these analyses to unpublished older (legacy) diclofenac trials. METHODS: We identified randomised controlled trials (RCTs) of diclofenac with planned study duration of at least 4 weeks for the treatment of osteoarthritis (OA) from 'legacy' studies conducted by Novartis but not published in a peer reviewed journal or included in any previous pooled analyses. All studies reporting efficacy and/or safety of treatment with diclofenac or other active therapies or placebo were included. We used a Bayesian NMA model, and estimated relative treatment effects between pairwise treatments. Main outcomes included pain relief measured using visual analogue scale at 2, 4 and 12 weeks and patient global assessment (PGA) at 4 and 12 weeks for efficacy, all-cause withdrawals, and adverse events. RESULTS: A total of 19 RCTs (5030 patients) were included; 18 of which were double-blind and one single-blind. All studies were conducted before cyclooxygenase 2 inhibitors (COXIBs) became commercially available. Data permitted robust efficacy comparison between diclofenac and ibuprofen, but the amount of data for other comparators was limited. Diclofenac 150mg/day was more efficacious than ibuprofen 1200mg/day and had likely favourable outcomes for pain relief compared to ibuprofen 2400mg/day. Diclofenac 100mg/day had likely favourable outcomes compared to ibuprofen 1200mg/day in alleviating pain. Based on PGA, diclofenac 150mg/day was more efficacious and likely to be favourable than ibuprofen 1200mg/day and 2400mg/day, respectively. Risk of withdrawal due to all causes with diclofenac and ibuprofen were comparable. Diclofenac 150mg/day was likely to have favourable efficacy and comparable tolerability with diclofenac 100mg/day. Results comparing diclofenac and ibuprofen were similar to those from NMAs of published trials. CONCLUSIONS: Results from these unpublished 'legacy' studies were similar to those from NMAs of published trials. The favourable efficacy results of diclofenac compared to ibuprofen expand the amount of available evidence comparing these two NSAIDs. The overall benefit-risk profile of diclofenac was comparable to that of ibuprofen in OA. IMPLICATIONS: The present NMA results reassures that the older unpublished blinded trials have similar results compared to more recently published trials and also contributes to increase the transparency of clinical trials performed with diclofenac further back in the past.

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). METHODS: Aggregated VAS pain data at baseline and CFB at 2, 6 and 12 weeks were collected from a systematic literature review of 176 RCTs in OA and RA. The predictive ability of earlier assessments for longer-term pain reduction was estimated using correlation and regression analyses. Analysis was performed using the R software package for statistical programming, version 3.1.1. RESULTS: Appropriate data were available from 50 RCTs (22,854 patients). Correlations between time points were high (weighted correlation coefficients between 2 and 6 weeks, 0.84; between 2 and 12 weeks, 0.79; and between 6 and 12 weeks, 0.96). CFB at 6 weeks was highly predictive and close to CFB at 12 weeks (regression coefficient 0.9, 95 % confidence interval 0.9-1.0). CFB at 2 weeks was significantly associated with CFB at 12 (0.8, 0.7-0.8) and 6 weeks (0.9, 0.8-1.0). CONCLUSIONS: The results showed that early analgesic response measured by VAS for pain beyond 2 weeks of treatment with a particular NSAID is likely to be predictive of response at 12 weeks. Failure to achieve desired pain relief in OA and RA after 2 weeks should trigger reassessment of dosage and/or analgesic.

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 2.0 mg every 8 weeks after 5 initial monthly doses, 2q8) studies. The impact of adjusting for up to two baseline characteristics was assessed. RESULTS: All methods provided similar results. After adjustment for baseline best-corrected visual acuity and central retinal thickness, no statistically significant difference in average gain in baseline best-corrected visual acuity from baseline at month 12 was found between ranibizumab 0.5 mg and aflibercept 2q8. CONCLUSIONS: Weight optimization and regression methods are useful options to adjust for more than one baseline characteristic.

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. These are applied to a case study of natalizumab for the treatment of relapsing remitting multiple sclerosis. We focus on the practical considerations of applying these methods and give recommendations for visual presentation of results. In the case study, we found structured benefit-risk analysis to be a useful tool for structuring, quantifying, and communicating the relative benefit and safety profiles of drugs in a transparent, rational and consistent way. The two frameworks were similar. MCDA is a generic and flexible methodology that can be used to perform a structured benefit-risk in any common context. wNCB is a special case of MCDA and is shown to be equivalent to an extension of the number needed to treat (NNT) principle. It is simpler to apply and understand than MCDA and can be applied when all outcomes are measured on a binary scale.

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. The resulting treatment effects estimates are suitable for use within the MCDA setting, and it is possible to derive the distribution of the overall benefit-risk balance through Markov Chain Monte Carlo simulation. The method is illustrated using a case study of natalizumab for relapsing-remitting multiple sclerosis.

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. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data.

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. We illustrate the method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH). METHODS: Our method uses a random effects model for placebo improvements to include single-arm observational studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and single-arm observational studies, both identified by a systematic review. RESULTS: Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice. CONCLUSIONS: Our method for the inclusion of single-arm observational studies allows the performance of indirect comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs. We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method could be used in similar situations where treatment comparisons have not been possible due to restrictions to RCT evidence and where a mixture of aggregate data and IPD are available.

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. Efficacy outcomes assessed included: pain relief measured by visual analogue scale (VAS); Western Ontario McMaster Universities Arthritis Index (WOMAC) VAS or WOMAC Likert scale; physical functioning measured by WOMAC VAS or Likert scale; and patient global assessment (PGA) of disease severity measured on VAS or 5-point Likert scale. Safety outcomes included: Antiplatelet Trialists' Collaboration (APTC), major cardiovascular (CV) and major upper gastrointestinal (GI) events, and withdrawals. Data for each outcome were synthesized by a Bayesian network meta-analysis (NMA). For efficacy assessments, labelled doses for OA treatment were used for the base case while labelled doses for RA treatment were also included in the sensitivity analysis. Pooled data across dose ranges were used for safety. RESULTS: Efficacy, safety, and tolerability data were found for 146,524 patients in 176 studies included in the NMA. Diclofenac (150 mg/day) was likely to be more effective in alleviating pain than celecoxib (200 mg/day), naproxen (1000 mg/day), and ibuprofen (2400 mg/day), and similar to etoricoxib (60 mg/day); a lower dose of diclofenac (100 mg/day) was comparable to all other treatments in alleviating pain. Improved physical function with diclofenac (100 and 150 mg/day) was mostly comparable to all other treatments. PGA with diclofenac (100 and 150 mg/day) was likely to be more effective or comparable to all other treatments. All active treatments were similar for APTC and major CV events. Major upper GI events with diclofenac were lower compared to naproxen and ibuprofen, comparable to celecoxib, and higher than etoricoxib. Risk of withdrawal with diclofenac was lower compared to ibuprofen, similar to celecoxib and naproxen, and higher than etoricoxib. CONCLUSIONS: The benefit-risk profile of diclofenac was comparable to other treatments used for pain relief in OA and RA; benefits and risks vary in individuals and need consideration when making treatment decisions.

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. METHODS: Outcomes of change from baseline in BCVA for dexamethasone compatible with those available for ranibizumab, reported by 4 randomised controlled trials, were estimated by fitting a multinomial distribution model to the probability of a patient achieving outcomes in a range of changes from baseline in BCVA (numbers of letters) at month 1. A Bayesian indirect comparison multinomial model was then developed to compare treatments in the Branch RVO (BRVO) and Central RVO (CRVO) populations. RESULTS: The multinomial model had excellent fit to the observed results. With the Bayesian indirect comparison, the probabilities of achieving ≥20 letters, with 95% credible intervals, at month 1 in patients with BRVO were 0.191 (0.130, 0.261) with ranibizumab and 0.093 (0.027, 0.213) with dexamethasone. In patients with CRVO, probabilities were 0.133 (0.082, 0.195) (ranibizumab) and 0.063 (0.016, 0.153) (dexamethasone). Probabilities of a gain in ≥10 letters in BRVO patients were 0.500 (0.365, 0.650) v 0.459 (0.248, 0.724) and in CRVO patients 0.459 (0.332, 0.602) v 0.498 (0.263, 0.791) for ranibizumab and dexamethasone treatments respectively. The comparisons also favoured ranibizumab at month 6 although changes to therapies after month 3 may have introduced bias. CONCLUSION: The newly developed combination of multinomial and indirect Bayesian comparison models indicated a trend for ranibizumab association with a greater percentage of ME patients achieving visual gains than dexamethasone at months 1 and 6 in a common clinical context, although results were not classically significant. The method was a useful tool for comparisons of probability distributions between clinical trials that reported events on different categorical scales and estimates can be used to inform economic models.

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). For each measure, the efficacy of fingolimod was estimated by analyzing individual patient data from fingolimod phase 3 trials using methodologies from studies of other oral therapies. These data were then used to build binomial regression models, which adjusted for differences in baseline characteristics between the studies. Models predicted the indirect relative risk of achieving NEDA status for fingolimod versus dimethyl fumarate or teriflunomide in an average patient from their respective phase 3 trials. RESULTS: The estimated relative risks of achieving NEDA status for fingolimod versus placebo in a pooled fingolimod trial population were numerically greater (i.e., fingolimod more efficacious) than the estimated relative risks for dimethyl fumarate or teriflunomide versus placebo in each respective trial population. In indirect comparisons, the predicted relative risks for all composite measures were better for fingolimod than comparator when tested against the trial populations of those treated with dimethyl fumarate (relative risk, clinical: 1.21 [95% confidence interval 1.06-1.39]; overall: 1.67 [1.08-2.57]), teriflunomide 7 mg (clinical: 1.22 [1.02-1.46]; overall: 2.01 [1.38-2.93]) and teriflunomide 14 mg (clinical: 1.14 [0.96-1.36]; overall: 1.61 [1.12-2.31]). CONCLUSION: Our modeling approach suggests that fingolimod therapy results in a higher probability of NEDA than dimethyl fumarate and teriflunomide therapy when phase 3 trial data are indirectly compared and differences between trials are adjusted for.

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. We explored the use of a quantitative method (stochastic multi-criteria acceptability analysis) to demonstrate how uncertainties and preferences on multiple criteria can be integrated into a single measure to reduce cognitive burden and increase transparency in decision making. RESULTS: Our benefit-risk model found that warfarin is favourable compared with placebo for the primary prevention of stroke in patients with atrial fibrillation. This favourable benefit-risk balance is fairly robust to differences in preferences. The probability of a favourable benefit-risk for warfarin against placebo is high (0.99) in our model despite the high uncertainty of randomised clinical trial data. In this case study, we identified major challenges related to the identification of relevant benefit-risk criteria and taking into account the diversity and quality of evidence available to inform the benefit-risk assessment. CONCLUSION: The main challenges in applying formal methods for medical benefit-risk assessment for a marketed drug are related to outcome definitions and data availability. Data exist from many different sources (both randomised clinical trials and observational studies), and the variability in the studies is large.

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 1.7% with an estimated savings close to 2 million dollars in 2 years.

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. We consider the methods in a cost-effectiveness analysis of a CRT with covariate imbalance, unequal cluster sizes and a prognostic relationship that varied by treatment group. The cost-effectiveness results differed according to the approach for covariate adjustment. A simulation study then assessed the relative performance of methods for addressing systematic imbalance in baseline covariates. The simulations extended the case study and considered scenarios with different levels of confounding, cluster size variation and few clusters. Performance was reported as bias, root mean squared error and CI coverage of the incremental net benefit. Even with low levels of confounding, unadjusted methods were biased, but all adjusted methods were unbiased. Multilevel models performed well across all settings, and unlike the other methods, reported CI coverage close to nominal levels even with few clusters of unequal sizes.

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. METHODS: Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. RESULTS: Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. CONCLUSIONS: MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

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. METHODS: for these CEAs need to recognize both the correlation between costs and outcomes and that these data may be dependent on the cluster. General checklists and methodological guidance for critically appraising CEA ignore these issues. This article develops a new checklist and applies it in a systematic review of CEAs that use CRTs. METHODS: The authors developed a checklist for CEAs that use CRTs, informed by a conceptual review of statistical methods. This checklist included criteria such as whether the analysis allowed for both clustering and the correlation between individuals' costs and outcomes. The authors undertook a systematic literature review of full economic evaluations that used CRTs. The quality of studies was assessed with the new checklist and by the "Drummond checklist." RESULTS: The authors identified 62 papers that met the inclusion criteria. On average, studies satisfied 9 of the 10 criteria for the checklist but only 20% of criteria for the new checklist. More than 40% of studies adopted statistical methods that completely ignored clustering, and 75% disregarded any correlation between costs and outcomes. Only 4 studies employed appropriate statistical methods that allowed for both clustering and correlation. CONCLUSIONS: Most economic evaluations that use data from CRTs ignored clustering or correlation. Statistical methods that address these issues are available, and their use should be encouraged. The new checklist can supplement generic CEA guidelines and highlight where research practice can be improved.

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. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

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. It compares the sampling uncertainty from using either technique in a Monte Carlo simulation. The experiments are repeated varying the sample size and the skewness of costs in the population. The results showed that, even when data were highly skewed, both methods accurately estimated the true standard errors (SEs) when sample sizes were moderate to large (n>50), and also gave good estimates for small data sets with low skewness. However, when sample sizes were relatively small and the data highly skewed, using the CLT rather than the bootstrap led to slightly more accurate SEs. We conclude that while in general using either method is appropriate, the CLT is easier to implement, and provides SEs that are at least as accurate as the bootstrap.

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. Natural history data were drawn from a longitudinal cohort study. The effectiveness of the DMTs was evaluated through a systematic review. Utility data were taken from a study of patients with clinically definite MS in Nova Scotia. Resource use and cost data were derived from the Sonya Slifka database and associated literature. RESULTS: When based on placebo-controlled evidence, the marginal cost-effectiveness of interferon beta (IFNß) and glatiramer acetate compared to best supportive care is expected to be in excess of $100,000 per quality-adjusted life-year gained. When evidence from head-to-head trials is incorporated into the model, the cost-effectiveness of 6 MIU IFNß-1a is expected to be considerably less favorable. Treatment discontinuation upon progression to Expanded Disability Status Scale 7.0 is expected to improve the cost-effectiveness of all DMTs. CONCLUSIONS: Further research is required to examine the long-term clinical effectiveness and cost-effectiveness of these therapies. There is no definitive guidance in the United States concerning discontinuation of DMTs; this study suggests that the prudent use of a treatment discontinuation rule may considerably improve the cost-effectiveness of DMTs.

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. It is configured to simulate Phase 2b and 3 trials based on the efficacy of new drugs at the end of Phase 2a, evidence about the efficacy of existing treatments, and expert opinion regarding key safety criteria.The model evaluates the performance of different development programmes with respect to the duration of disease of the target population, Phase 2b and 3 sample sizes, the dose(s) of the experimental treatment, the choice of comparator, the duration of the Phase 2b clinical trial, the primary efficacy outcome and decision criteria for successfully passing Phases 2b and 3. It uses Bayesian clinical trial simulation to calculate the probability of successful drug registration based on the uncertainty about parameters of interest, thereby providing a more realistic assessment of the likely outcomes of individual trials and sequences of trials for the purpose of decision making.In this case study, the results show that, depending on the trial design, the new treatment has assurances of successful drug registration in the range 0.044-0.142 for an ACR20 outcome and 0.057-0.213 for an ACR50 outcome.