Efficacy and safety of diclofenac in osteoarthritis: Results of a network meta-analysis of unpublished legacy studies.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 The present NMA extends these analyses to unpublished older (legacy) diclofenac trials.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 CONCLUSIONS: Results from these unpublished 'legacy' studies were similar to those from NMAs of published trials.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.

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.

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.

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.

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. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration.

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. METHODS: Data were drawn from meta-analysis of randomized controlled trials and from a large longitudinal outcomes databank. Uncertainty was addressed using probabilistic and one-way sensitivity analyses. A lifetime horizon and Medicare viewpoint were adopted. RESULTS: In the base case analysis, anakinra was the least effective and least costly strategy. Etanercept, adalimumab, and infliximab were similar in terms of effectiveness, but infliximab was more costly. If decision makers are willing to pay a maximum of $50,000/QALY, the probability that infliximab is cost-effective is <1%. Findings were robust to a range of sensitivity analyses. Only if the dose of infliximab remains constant over time is this likely to be a cost-effective strategy. CONCLUSION: Infliximab is unlikely to be cost-effective in the Medicare population compared with either etanercept or adalimumab. Anakinra is substantially less costly but is also less effective than the 3 tumor necrosis factor alpha inhibitors.

Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study.

Differences in the mean, spread and skewness of cost data collected from different countries present problems for analysis and interpretation. Here we develop generalised linear multilevel models to estimate the effects of patient and national characteristics on costs. Using gamma distributions and multiplicative effects for patient characteristics fitted the data better than models which assumed normal distributions or estimated additive effects. A multilevel gamma model is employed to allow for heterogeneity in the effects of patient case-mix across centres. Analysis of multinational cost data must recognise differences in mean, spread and skewness across centres, as well as the data's hierarchical structure.

How sensitive are cost-effectiveness analyses to choice of parametric distributions?

BACKGROUND: Cost-effectiveness analyses of clinical trial data are based on assumptions about the distributions of costs and effects. Cost data usually have very skewed distributions and can be difficult to model. The authors investigate whether choice of distribution can make a difference to the conclusions drawn. METHODS: The authors compare 3 distributions for cost data-normal, gamma, and lognormal-using similar parametric models for the cost-effectiveness analyses. Inferences on the cost-effectiveness plane are derived, together with cost-effectiveness acceptability curves. These methods are applied to data from a trial of rapid magnetic resonance imaging (rMRI) investigation in patients with low back pain. RESULTS: The gamma and lognormal distributions fitted the cost data much better than the normal distribution. However, in terms of inferences about cost-effectiveness, it was the normal and gamma distributions that gave similar results. Using the lognormal distribution led to the conclusion that rMRI was cost-effective for a range of willingness-to-pay values where assuming a gamma or normal distribution did not. CONCLUSIONS: Conclusions from cost-effectiveness analyses are sensitive to choice of distribution and, in particular, to how the upper tail of the cost distribution beyond the observed data is modeled. How well a distribution fits the data is an insufficient guide to model choice. A sensitivity analysis is therefore necessary to address uncertainty about choice of distribution.

Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations.

BACKGROUND: Overall assessments of cost-effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost-effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed. METHODS: We propose a coherent set of Bayesian methods to extend cost-effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost-effectiveness plane, and as cost-effectiveness acceptability curves. RESULTS: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost-effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost-effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them. CONCLUSIONS: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability--to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making.

Imputation of a true endpoint from a surrogate: application to a cluster randomized controlled trial with partial information on the true endpoint.

BACKGROUND: The Anglia Menorrhagia Education Study (AMES) is a randomized controlled trial testing the effectiveness of an education package applied to general practices. Binary data are available from two sources; general practitioner reported referrals to hospital, and referrals to hospital determined by independent audit of the general practices. The former may be regarded as a surrogate for the latter, which is regarded as the true endpoint. Data are only available for the true end point on a sub set of the practices, but there are surrogate data for almost all of the audited practices and for most of the remaining practices. METHODS: The aim of this paper was to estimate the treatment effect using data from every practice in the study. Where the true endpoint was not available, it was estimated by three approaches, a regression method, multiple imputation and a full likelihood model. RESULTS: Including the surrogate data in the analysis yielded an estimate of the treatment effect which was more precise than an estimate gained from using the true end point data alone. CONCLUSIONS: The full likelihood method provides a new imputation tool at the disposal of trials with surrogate data.

Bayesian evaluation of breast cancer screening using data from two studies.

The mean sojourn time (the duration of the period during which a cancer is symptom free but potentially detectable by screening) and the screening sensitivity (the probability that a screen applied to a cancer in the preclinical screen detectable period will result in a positive diagnosis) are two important features of a cancer screening programme. Little data from any single study are available on the potential effectiveness of mammographic screening for breast cancer in women with a family history of the disease, despite this being an important public health issue. We develop a method of estimation, from two separate studies, of the two parameters, assuming that transition from no disease to preclinical screen detectable disease, and from preclinical disease to clinical disease, are Poisson processes. Estimation is performed by a Markov chain Monte Carlo algorithm. The method is applied to the synthesis of two studies of mammographic screening in women with a family history of breast cancer, one in Manchester and one in Kopparberg, Sweden.

A comparison of clinical assessment with ultrasound in the management of secondary postpartum haemorrhage.

OBJECTIVE: To compare the diagnostic accuracy of clinical assessment with transabdominal ultrasound in the management of secondary postpartum haemorrhage (PPH). DESIGN: A prospective cohort study. METHODS: Fifty-three women who presented to a teaching hospital obstetric unit with secondary PPH were studied. Patients were divided into those in whom retained placental tissue was or was not the suspected cause of bleeding. This diagnosis was based on history/examination and transabdominal pelvic ultrasound scan. The definitive diagnosis was made following uterine evacuation or was assumed in women who stopped bleeding without surgical management. Likelihood ratio (LR) was used as an accuracy measure. RESULTS: The positive LR for clinical assessment was 5.5 (95% CI 2.7-12.1) compared with 2.4 (95% CI 1.5-3.7) for ultrasound. The negative LRs were 0.1 (95% CI 0.04-0.5) and 0.1 (95% CI 0.02-0.5) for clinical and ultrasound assessment, respectively. CONCLUSION: Clinical examination and ultrasound scan assessment have limited diagnostic accuracy in secondary PPH.