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We aim to utilise real world data in evidence synthesis to optimise an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis in order to allow for evidence on first-line therapies to inform second-line effectiveness estimates. We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA) to supplement RCT evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first and second-line treatments. Summary data were obtained from 21 trials of biologic therapies including 2 for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line RCTs. Bivariate NMA provides effectiveness estimates for all treatments in first- and second-line, including predicted effects in second-line where these estimates did not exist in the data. This novel methodology may have further applications, for example for bridging networks of trials in children and adults. [Abstract copyright: Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.]

Background and Aim: Findings from the literature suggest that the validity of surrogate endpoints in metastatic colorectal cancer (mCRC) may depend on a treatments’ mechanism of action. We explore this and the impact of Kirsten rat sarcoma (KRAS) status on surrogacy patterns in mCRC. Methods: A systematic review was undertaken to identify randomized controlled trials (RCTs) for pharmacological therapies in mCRC. Bayesian meta-analytic methods for surrogate endpoint evaluation were used to evaluate surrogate relationships across all RCTs, by KRAS status and treatment class. Surrogate endpoints explored were progression free survival (PFS) as a surrogate endpoint for overall survival (OS), and tumour response (TR) as a surrogate for PFS and OS. Results: 66 RCTs were identified from the systematic review. PFS showed a strong surrogate relationship with OS across all data and in subgroups by KRAS status. The relationship appeared stronger within individual treatment classes compared to the overall analysis. The TR-PFS and TR-OS relationships were found to be weak overall but stronger within the Epidermal Growth Factor Receptor + Chemotherapy (EGFR + Chemo) treatment class; both overall and in the wild type (WT) patients for TR-PFS, but not in patients with the mutant (MT) KRAS status where data were limited. Conclusions: PFS appeared to be a good surrogate endpoint for OS. TR showed a moderate surrogate relationship with PFS and OS for the EGFR + Chemo treatment class. There was some evidence of impact of the mechanism of action on the strength of the surrogacy patterns in mCRC, but little evidence of the impact of KRAS status on the validity of surrogate endpoints.

Bivariate meta-analysis provides a useful framework for combining information across related studies and has been utilized to combine evidence from clinical studies to evaluate treatment efficacy on two outcomes. It has also been used to investigate surrogacy patterns between treatment effects on the surrogate endpoint and the final outcome. Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final outcome and to predict clinical benefit or harm. The standard bivariate meta-analytic approach models the observed treatment effects on the surrogate and the final outcome outcomes jointly, at both the within-study and between-studies levels, using a bivariate normal distribution. For binomial data, a normal approximation on log odds ratio scale can be used. However, this method may lead to biased results when the proportions of events are close to one or zero, affecting the validation of surrogate endpoints. In this article, we explore modeling the two outcomes on the original binomial scale. First, we present a method that uses independent binomial likelihoods to model the within-study variability avoiding to approximate the observed treatment effects. However, the method ignores the within-study association. To overcome this issue, we propose a method using a bivariate copula with binomial marginals, which allows the model to account for the within-study association. We applied the methods to an illustrative example in chronic myeloid leukemia to investigate the surrogate relationship between complete cytogenetic response and event-free-survival.

Objective Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE). Methods We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS. Results Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS. Conclusion The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC. Highlights Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug’s mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited. Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial. Careful selection of RWE is needed to reduce risk of bias.

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models’ performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

Background Network meta-analysis synthesises data from a number of clinical trials in order to assess the comparative efficacy of multiple healthcare interventions in similar patient populations. In situations where clinical trial data are heterogeneously reported i.e. data are missing for one or more outcomes of interest, synthesising such data can lead to disconnected networks of evidence, increased uncertainty, and potentially biased estimates which can have severe implications for decision-making. To overcome this issue, strength can be borrowed between outcomes of interest in multivariate network meta-analyses. Furthermore, in situations where there are relatively few trials informing each treatment comparison, there is a potential issue with the sparsity of data in the treatment networks, which can lead to substantial parameter uncertainty. A multivariate network meta-analysis approach can be further extended to borrow strength between interventions of the same class using hierarchical models. Methods We extend the trivariate network meta-analysis model to incorporate the exchangeability between treatment effects belonging to the same class of intervention to increase precision in treatment effect estimates. We further incorporate a missing data framework to estimate uncertainty in trials that did not report measures of variability in order to maximise the use of all available information for healthcare decision-making. The methods are applied to a motivating dataset in overactive bladder syndrome. The outcomes of interest were mean change from baseline in incontinence, voiding and urgency episodes. All models were fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS. Results All models (univariate, multivariate, and multivariate models incorporating class effects) produced similar point estimates for all treatment effects. Incorporating class effects in multivariate models often increased precision in treatment effect estimates. Conclusions Multivariate network meta-analysis incorporating class effects allowed for the comparison of all interventions across all outcome measures to ameliorate the potential impact of outcome reporting bias, and further borrowed strength between interventions belonging to the same class of treatment to increase the precision in treatment effect estimates for healthcare policy and decision-making.

BackgroundIn the appraisal of clinical interventions, complex evidence synthesis methods, such as network meta-analysis (NMA), are commonly used to investigate the effectiveness of multiple interventions in a single analysis. The results from a NMA can inform clinical guidelines directly or be used as inputs into a decision-analytic model assessing the cost-effectiveness of the interventions. However, there is hesitancy in using complex evidence synthesis methods when evaluating public health interventions. This is due to significant heterogeneity across studies investigating such interventions and concerns about their quality. Threshold analysis has been developed to help assess and quantify the robustness of recommendations made based on results obtained from NMAs to potential limitations of the data. Developed in the context of clinical guidelines, the method may prove useful also in the context of public health interventions. In this paper, we illustrate the use of the method in the study investigating the effectiveness of interventions aiming to increase the uptake of poison prevention behaviours in homes with children aged 0-5.MethodsRandom effects NMA was carried out to assess the effectiveness of several interventions for increasing the uptake of poison prevention behaviours, focusing on the safe storage of other household products outcome. Threshold analysis was then applied to the NMA to assess the robustness of the intervention recommendations made based on the NMA.Results15 studies assessing seven interventions were included in the NMA. The results of the NMA indicated that complex intervention, including Education, Free/low-cost equipment, Fitting equipment and Home safety inspection, was the most effective intervention at promoting poison prevention behaviours. However, the threshold analyses highlighted that this intervention recommendation was not robust.Conclusions In our case study, threshold analysis allowed us to demonstrate that the intervention recommendation for promoting poison prevention behaviours was not robust to changes in the evidence due to potential bias. Therefore, caution should be taken when considering such interventions in practice. We have illustrated the potential benefit of threshold analysis and, therefore, encourage the use of the method in practice as a sensitivity analysis for NMA of public health interventions.

IntroductionRandomised controlled trials (RCTs) may use surrogate endpoints as substitutes and predictors of patient-relevant/participant-relevant final outcomes (eg, survival, health-related quality of life). Translation of effects measured on a surrogate endpoint into health benefits for patients/participants is dependent on the validity of the surrogate; hence, more accurate and transparent reporting on surrogate endpoints is needed to limit misleading interpretation of trial findings. However, there is currently no explicit guidance for the reporting of such trials. Therefore, we aim to develop extensions to the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines to improve the design and completeness of reporting of RCTs and their protocols using a surrogate endpoint as a primary outcome.Methods and analysisThe project will have four phases: phase 1 (literature reviews) to identify candidate reporting items to be rated in a Delphi study; phase 2 (Delphi study) to rate the importance of items identified in phase 1 and receive suggestions for additional items; phase 3 (consensus meeting) to agree on final set of items for inclusion in the extensions and phase 4 (knowledge translation) to engage stakeholders and disseminate the project outputs through various strategies including peer-reviewed publications. Patient and public involvement will be embedded into all project phases.Ethics and disseminationThe study has received ethical approval from the University of Glasgow College of Medical, Veterinary and Life Sciences Ethics Committee (project no: 200210051). The findings will be published in open-access peer-reviewed publications and presented in conferences, meetings and relevant forums.

ObjectivesUniversity student cohorts have a potential for significant impacts on public health policies. Health impacts arise from wide geographic catchment areas and behavioural patterns that enhance infectious disease spread and occasional cases of meningococcal meningitis and septicaemia, measles and mumps. Universities and the Department of Health and Social Care have tackled these serious problems through advertising campaigns and by offering free MenACWY and MMR vaccines to university students. Our study aimed to assess the engagement of universities with these vaccine campaigns and student awareness of this information. Study DesignInformation was accrued by a combination of e-mail and telephone interactions with welfare officers at universities. Student perceptions of meningitis vaccine campaigns were studied through use of questionnaires with University of Leicester students. ResultsInformation provided by 17 universities indicated that all universities run meningitis awareness campaigns whereas on campus meningitis campaigns were infrequent and of variable penetration into student cohorts. Assessment of 272 students from a 2019-2020 cohort found that 17.5% and 58% of students did not know or had not had the MMR and MenACWY vaccines. Only 37% of students were aware that these vaccines were free and available from a university-linked GP practice with lack of this knowledge being significantly associated with uncertainty or perceived absence of immunisation. This latter group were significantly associated with a preference for on campus immunisation. DiscussionThis information is important for understanding how to target a critical cohort with effective campaigns for uptake of meningitis, MMR and COVID-19 vaccines.

Background In the appraisal of clinical interventions, complex evidence synthesis methods, such as network meta-analysis (NMA), are commonly used to investigate the effectiveness of multiple interventions in a single analysis. The results from a NMA can inform clinical guidelines directly or be used as inputs into a decision-analytic model assessing the cost-effectiveness of the interventions. However, there is hesitancy in using complex evidence synthesis methods when evaluating public health interventions. This is due to significant heterogeneity across studies investigating such interventions and concerns about their quality. Threshold analysis has been developed to help assess and quantify the robustness of recommendations made based on results obtained from NMAs to potential limitations of the data. Developed in the context of clinical guidelines, the method may prove useful also in the context of public health interventions. In this paper, we illustrate the use of the method in public health, investigating the effectiveness of interventions aiming to increase the uptake of accident prevention behaviours in homes with children aged 0–5. Methods Two published random effects NMAs were replicated and carried out to assess the effectiveness of several interventions for increasing the uptake of accident prevention behaviours, focusing on the safe storage of other household products and stair gates outcomes. Threshold analysis was then applied to the NMAs to assess the robustness of the intervention recommendations made based on the results from the NMAs. Results The results of the NMAs indicated that complex intervention, including Education, Free/low-cost equipment, Fitting equipment and Home safety inspection, was the most effective intervention at promoting accident prevention behaviours for both outcomes. However, the threshold analyses highlighted that the intervention recommendation was robust for the stair gate outcome, but not robust for the safe storage of other household items outcome. Conclusions In our case study, threshold analysis allowed us to demonstrate that there was some discrepancy in the intervention recommendation for promoting accident prevention behaviours as the recommendation was robust for one outcome but not the other. Therefore, caution should be taken when considering such interventions in practice for the prevention of poisonings in homes with children aged 0–5. However, there can be some confidence in the use of this intervention in practice to promote the possession of stair gates to prevent falls in homes with children under 5. We have illustrated the potential benefit of threshold analysis in the context of public health and, therefore, encourage the use of the method in practice as a sensitivity analysis for NMA of public health interventions.

Surrogate endpoints are very important in regulatory decision-making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on the pairwise methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods which combine data on treatment effects on the surrogate and final outcomes, from trials investigating heterogeneous treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the surrogacy patterns across multiple trials (different populations) within a treatment contrast and across treatment contrasts, thus enabling predictions of the treatment effect on the final outcome for a new study in a new population or investigating a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using simulated data and an illustrative example in advanced colorectal cancer. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatments for which surrogacy holds, thus leading to better predictions.

In June 2021, the US Food and Drug Administration granted accelerated approval to aducanumab for treating Alzheimer’s disease based on the drug’s amyloid reducing effects. This was despite evidence from several earlier studies that shrinkage of β-amyloid protein plaques does not predictably delay cognitive impairment.1 The controversial decision has drawn attention to the use of surrogate endpoints—laboratory values, radiographic images, or other physical measures that may serve as indicators of clinical outcomes such as symptom control or mortality—in clinical trials of new drugs.2 In fact, the approval of aducanumab is only the latest example of growing regulatory reliance on surrogate endpoints, even though their use can cause problems for patients, clinicians, drug regulators, and health technology assessment bodies. We argue for more selective use of surrogate endpoints when evaluating new drugs, restricting their use to chronic diseases, especially when collecting data on patient relevant clinical outcomes requires trials with unattainably long follow up.

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. We investigate methods for the inclusion of RWE in NMA and its impact on the uncertainty around the effectiveness estimates. Methods: A range of methods for inclusion of RWE in evidence synthesis, including Bayesian hierarchical and power prior models, were investigated by applying them to an example in relapsing remitting multiple sclerosis. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. Power prior NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs; however, this also increased the level of uncertainty. Conclusion: The power prior approach for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted., 24 pages, 3 figures

Progression free survival (PFS) and tumour response (TR) have been investigated as surrogate endpoints for overall survival (OS) in advanced colorectal cancer (aCRC), however their validity has been shown to be suboptimal. In recent years, meta-analytic methods allowing for use of multiple surrogate endpoints jointly have been proposed. The aim of this research was to assess if PFS and TR used jointly as surrogate endpoints to OS improve their predictive value. Data were obtained from a systematic review of randomised controlled trials investigating effectiveness of different pharmacological therapies in aCRC: systemic chemotherapies, anti-epidermal growth factor receptor therapies, anti-angiogenic agents, other multi-targeted antifolate treatments and intra-hepatic arterial chemotherapy. Multivariate meta-analysis was used to model the association patterns between treatment effects on the surrogate endpoints (PFS, TR) and the final outcome (OS). Analysis of 33 trials which reported treatment effects on all three outcomes showed reasonably strong association between treatment effects on PFS and OS. A weak surrogate relationship was noted between the treatment effects on TR and OS. Modelling the two surrogate endpoints, TR and PFS, jointly as predictors of OS gave no marked improvement in neither surrogacy patterns nor the precision of predicted treatment effect in the cross-validation procedure. When investigating subgroups of therapy, only small improvement in precision of predicted treatment effects on the final outcome in studies investigating anti-angiogenic therapy was noted. Overall, the simultaneous modelling of two surrogate endpoints did not lead to improvement in association between treatment effects on surrogate and final endpoints in aCRC.

IntroductionThe use of real-world data, as an alternative to randomized controlled trials, is becoming increasingly common in the evaluation of new health technologies. With this rise in real-world literature, such data will also enter evidence synthesis models. While it can be beneficial to utilize data from all available sources, this can introduce the problem of double-counting of participants.MethodsUsing a number of case-studies, we discuss and illustrate various issues around double-counting. These include synthesis of studies using the same database or the same subset of participants, overlapping use of intervention arms across studies and the use of registry data from the participants overlapping with those in randomized controlled trials. The implications in research are considered along with common methods used currently to overcome these issues.ResultsDouble-counting of participants in evidence synthesis can artificially inflate precision, potentially leading to inappropriate conclusions. Common methods currently used to help mitigate the impact of double-counting includes stratifying analysis to different timelines, using the most comprehensive study in the evidence synthesis model or using the study that has the largest sample size. However, in all of these cases, sensitivity analyses would need to be considered to ensure robust results.ConclusionsCurrently, there are no published guidelines on how to address the issue of double-counting. With the increased use of real-world data in evidence synthesis, double-counting has the potential to become a significant issue. Therefore, it is of significant importance that methodologies and guidelines are developed to address this.

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (1986). We investigate the use of our proposed methods in the context of a real example.

Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final clinical outcome and to predict clinical benefit or harm. Such endpoints are assessed for their predictive value of clinical benefit by investigating the surrogate relationship between treatment effects on the surrogate and final outcomes using meta-analytic methods. When surrogate relationships vary across treatment classes, such validation may fail due to limited data within each treatment class. In this paper, two alternative Bayesian meta-analytic methods are introduced which allow for borrowing of information from other treatment classes when exploring the surrogacy in a particular class. The first approach extends a standard model for the evaluation of surrogate endpoints to a hierarchical meta-analysis model assuming full exchangeability of surrogate relationships across all the treatment classes, thus facilitating borrowing of information across the classes. The second method is able to relax this assumption by allowing for partial exchangeability of surrogate relationships across treatment classes to avoid excessive borrowing of information from distinctly different classes. We carried out a simulation study to assess the proposed methods in nine data scenarios and compared them with subgroup analysis using the standard model within each treatment class. We also applied the methods to an illustrative example in colorectal cancer which led to obtaining the parameters describing the surrogate relationships with higher precision.

BackgroundWhen designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme.MethodsIn our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations.ResultsInclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme.ConclusionThe use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE.

In health technology assessment, decisions are based on complex cost-effectiveness models that require numerous input parameters. When not all relevant estimates are available, the model may have to be simplified. Multiparameter evidence synthesis combines data from diverse sources of evidence, which results in obtaining estimates required in clinical decision making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multistate Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a 3-state Markov model comprising stable disease, progressive disease, and dead states, while lack of the estimate restricted the model to a 2-state model (with alive and dead states). The 2-state and 3-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the 3-state model: £22,148 per quality-adjusted life year gained compared to £30,026 obtained from the 2-state model) and the expected value of perfect information (which increased with the 3-state model). The 3-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway in more detail while helping to prevent valuable clinical data from being discarded.

Objectives To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. Study Design and Settings The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. Results Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify “top-ranking” interventions. Conclusions The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician.

Background: In health technology assessment, decisions about reimbursement for new health technologies are largely based on effectiveness estimates. Sometimes, however, the target effectiveness estimates are not readily available. This may be because many alternative instruments measuring these outcomes are being used (and not all always reported) or an extended follow-up time of clinical trials is needed to evaluate long-term end points, leading to the limited data on the target clinical outcome. In the areas of highest priority in health care, decisions are required to be made on a short time scale. Therefore, alternative clinical outcomes, including surrogate end points, are increasingly being considered for use in evidence synthesis as part of economic evaluation. Objective: To illustrate the potential effect of reduced uncertainty around the clinical outcome on the utility when estimating it from a multivariate meta-analysis. Methods: Bayesian multivariate meta-analysis has been used to synthesize data on correlated outcomes in rheumatoid arthritis and to incorporate external data in the model in the form of informative prior distributions. Estimates of Health Assessment Questionnaire were then mapped onto the health-related quality-of-life measure EuroQol five-dimensional questionnaire, and the effect was compared with mapping the Health Assessment Questionnaire obtained from the univariate approach. Results: The use of multivariate meta-analysis can lead to reduced uncertainty around the effectiveness parameter and ultimately uncertainty around the utility. Conclusions: By allowing all the relevant data to be incorporated in estimating clinical effectiveness outcomes, multivariate meta-analysis can improve the estimation of health utilities estimated through mapping methods. While reduced uncertainty may have an effect on decisions based on economic evaluation of new health technologies, the use of short-term surrogate end points can allow for early decisions. More research is needed to determine the circumstances under which uncertainty is reduced.

Objectives To establish current guidance and practice in UK on presentation of indirect comparison and mixed treatment comparison analyses; to provide recommendations to improve indirect comparison/mixed treatment comparison reporting and to identify research priorities for improved presentation. Methods Existing institutional guidance for conducting indirect comparison/mixed treatment comparison alongside current practice in health technology assessment was reviewed. Reports published in UK by the Health Technology Assessment programme since 1997, which utilized indirect comparison/mixed treatment comparison methods, were reviewed with respect to the presentation of study data, statistical models and results. Recommendations for presentation were developed. Results Guidance exists that provide the details necessary to conduct a successful indirect comparison/mixed treatment comparison analysis but recommendations on presentation are limited. Of 205 health technology assessment reports that contained evidence synthesis for effectiveness, 19 used indirect comparison/mixed treatment comparison methods. These reports utilized numerous presentational formats from which the following key components were identified: network table/diagram for presenting data; model description to allow reproducibility; and tables, forest plots, matrix tables and summary forest plots for presenting a range of results. Recommendations were developed to ensure that reporting is explicit, transparent and reproducible. Approaches most understandable by non-technical decision makers, and areas where future research is required, are outlined. Conclusions There is no standard presentational style used in UK for reporting indirect comparison/mixed treatment comparison, and the use of graphical tools is limited. Standardization of reporting and innovation in graphical representation of indirect comparison/mixed treatment comparison results is required.

Objectives:The aim of this study was to examine the use of implicit and explicit Bayesian methods in health technology assessments and to identify whether this has changed over time.Methods:A review of all health technology assessment (HTA) reports of secondary research published by the UK National Institute of Health Research (NIHR) between 1997 and 2011. Data were extracted on the use and implementation of Bayesian methods, whether defined as such by the original authors (i.e., explicit) or not (i.e., implicit).Results: A total of 155 of 375 (41 percent) NIHR HTA reports, identified as relevant to this review, contained a Bayesian analysis. Of these, 128 (83 percent) contained an implicit Bayesian analysis, 3 (2 percent) an explicit Bayesian analysis and 24 (15 percent) both implicit and explicit Bayesian analyses. Of the twenty-seven reports that explicitly used Bayes theorem, only six included prior information in the form of (informative) prior distributions. Over time, the percentage of HTA reports that used Bayesian (implicit and/or explicit) methods increased from 0 percent in 1997 to nearly 80 percent in 2011.Conclusions: This review has shown that there has been an increase in the use of Bayesian methods in HTA, which is likely to be a result of the increase in freely available resources to implement the approach. Areas where Bayesian methods have the potential to advance healthcare evaluations in the future are considered in the discussion.

Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent ‘vague’ prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest. © 2013 The Authors. Statistics inMedicine Published by John Wiley & Sons, Ltd.

A number of meta‐analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta‐analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta‐analytic framework, the between‐study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between‐study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between‐study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual‐level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (1986). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

Objective Outcomes for any mental health service will vary with the characteristics of those admitted as well as with the clinical provision of the service itself. This study aims to explore, for a medium secure forensic service in England, temporal changes in (1) characteristics of those admitted and (2) outcome after discharge and (3) to examine whether such changes are related. Method Baseline characteristics and reconviction outcomes were derived from multiple data sources for 550 first admissions to a medium secure forensic unit for a 20-year period. Time to reconviction was examined using Kaplan–Meier analysis and Cox regression. Results Over time, severity of admissions increased, as did discharges to prison; discharges to non-secure hospitals reduced. Risk of reconviction increased by 3.9%–4.2% for each year of admission from 1983, which was explained by the increased admission of higher-risk patients. Conclusion This medium secure service admitted patients with increasing levels of risk; reoffending rates reflect admission characteristics. Service funding decisions should take account of the characteristics of those admitted. Significant outcomes This study indicates that the profile of patients admitted over a 20-year period increased in severity. Over time, reconviction after discharge occurred earlier after release. This increase in reconviction was explained by the type of patient admitted. Limitations Examination of a cohort from a single medium secure unit limits the generalizability of the findings. The study focuses on a criminological outcome measure (i.e. reconviction); other domains may be equally relevant (e.g. the relief of psychological distress). Examining an entire series of admissions introduces heterogeneity by, for example, considering the outcome of men and women together. Copyright © 2012 John Wiley & Sons, Ltd.

Objectives. To systematically review and meta-analyse evidence on the effectiveness of the TNF-α inhibitors when used sequentially. Methods. Systematic review of comparative and single-arm observational studies. Data were synthesized using random-effects meta-analysis. Treatment effects were estimated using four outcome measures from the included studies: European League Against Rheumatism (EULAR) and ACR20 response rates and mean improvement in disease activity score-28 (DAS-20) and HAQ. The effect of other factors was explored via meta-regression and sub-group analyses. Results. Twenty studies comprising 2705 patients were included in the analysis. All studies were observational and most had no control group. Therefore, our primary analysis considered patient changes from baseline. The mean percentage of ACR20 responders was 60.8% (95% CI 53.8, 67.4), EULAR responders 70.5% (95% CI 63.7, 76.6), mean overall improvement in DAS-28 scores was 1.53 (95% CI 1.25, 1.80) and in HAQ scores was 0.25 (95% CI 0.11, 0.40). Four studies made comparisons with patients who received TNF-α inhibitors for the first time. Response rates associated with sequential TNF-α inhibitor treatment were lower than for first-time use. Conclusions. Sequential TNF-α inhibitor use is likely to lead to treatment benefit in terms of the signs and symptoms of disease and physical function. There is also some evidence to suggest that the probability of achieving a response is lower, and the average magnitude of response is lower than the first use. Further evidence from randomized controlled trials is required to confirm and further quantify the role specific anti-TNF-α agents have when used sequentially.

Background The aim of this study was to identify the nature and severity of nutritional problems associated with the current treatment of medulloblastoma and to identify any risk factors for nutritional morbidity during treatment. Procedure A multicentre retrospective audit of medical and dietetic notes of 41 children treated for medulloblastoma in three UK paediatric oncology centres was undertaken. Data on nutritional status, nutritional support, mutism, swallowing and common toxicity criteria (CTC) scores for vomiting, constipation and mobility were collected at defined points in treatment from diagnosis until 12 months post-treatment. Results Significant problems including weight loss, vomiting and constipation were highlighted early on in treatment. The majority of patients were well nourished at diagnosis with a mean percentage weight: height of 99.8%, however nutritional status started to decline early in treatment during radiotherapy, coinciding with 49% of patients having grade 1 or above CTC score for vomiting and constipation. The decline in nutritional status continued, peaking by course 2 of chemotherapy with a mean weight loss of 8.2% since diagnosis. Proactive supplementary feeding early in treatment by one of the three centres demonstrated a superior nutritional outcome when compared statistically to the two centres that fed only as a response to nutritional decline. Conclusion The study highlighted significant morbidity associated with the current treatment of medulloblastoma. Findings suggest the need to consider earlier proactive nutritional intervention to prevent nutritional decline during treatment. These early nutritional problems may be related to toxicities of radiotherapy and concomitant vincristine. Pediatr Blood Cancer 2009;53:570–575. © 2009 Wiley-Liss, Inc.

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions.