IntroductionIn the health technology assessment (HTA) of biologic treatments for rheumatoid arthritis (RA), there is limited randomized evidence on treatment effectiveness after first-line treatment failure. We demonstrate how real-world data (RWD) could fill this evidence gap.MethodsTarget trial emulation (TTE) minimizes biases in the causal analysis of RWD by prespecifying a protocol for a hypothetical randomized clinical trial (RCT) that would estimate the effect of interest. The application of TTE for HTA was illustrated using RWD from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to estimate the effectiveness of rituximab versus nonbiologic therapy (NBT) after first-line biologic failure, in terms of European Alliance of Associations for Rheumatology response achievement. The effectiveness estimates from RWD were combined with RCT estimates in a meta-analysis. The pooled estimates were entered into an economic model to estimate the incremental cost-effectiveness ratio (ICER) comparing biologic versus NBT strategies.ResultsBased on RWD, rituximab was associated with higher probabilities of achieving a moderate or good response (0.215 v. 0.174) and a good response (0.090 v. 0.066) as compared with NBT. These probabilities were lower than those estimated from RCT data (moderate or good 0.650; good 0.150). The economic model estimated less time on treatment and lower costs associated with biologics when based on RWD compared with RCT data (mean £63,500 v. £70,000). This resulted in a higher ICER based on RWD compared with RCT data (mean £46,800 v. £34,700 per quality-adjusted life-year gained).ConclusionsRWD can provide supplemental evidence on treatment effectiveness where randomized evidence is limited. This can make a meaningful difference to cost-effectiveness estimates. Our results are not intended to inform current RA management.HighlightsIn health technology assessment, real-world data (RWD) can provide supplemental evidence on treatment effectiveness where there is limited randomized evidence.Target trial emulation was applied using RWD to estimate the clinical effectiveness of biologic treatment; these estimates were combined with estimates from an RCT in a meta-analysis, and the pooled estimates were entered into an economic model for rheumatoid arthritis.Treatment effect estimates based on combining RWD and RCT data were more modest compared with the effectiveness estimates from the RCT data alone, leading to a difference in the estimate of cost-effectiveness comparing biologics with nonbiologic therapy.

Estimating and interpreting treatment effects (TE) for rare or delayed clinical outcomes is often challenging. To address this, researchers may incorporate additional evidence sources, including historical trial data and concurrent information from intermediate outcomes. In this article, we present Bayesian dynamic borrowing (BDB) as a principled framework for integrating such data while maintaining control of bias and Type I error. Using hypothetical trials of a novel high-efficacy therapy for multiple sclerosis, we provide a step-by-step demonstration of how BDB can be used to combine an imprecise TE estimate for a final outcome with a prediction derived from historical data and information on a concurrent intermediate outcome. Our illustration includes calibration of BDB to meet desired Type I error and power properties, and sensitivity analyses to assess robustness to assumption violations. We also discuss key considerations for applying BDB in regulatory decision making and health technology assessment contexts.

Background: Often when undertaking meta-analyses of time-to-event (TTE) outcomes, especially in a Health Technology Assessment context, a hazard ratio (HR) scale is used. However, issues arise when there is evidence of non-proportional hazards in some of the studies included. A number of methods have been advocated, but their use has been limited by either their complexity and/or the ease with which their results can be used in HTA. An alternative approach is to assume a treatment-log(time) interaction within a Cox proportional hazards model for each study, and to then undertake a bivariate meta-analysis of the resulting treatment and interaction coefficients, so that an overall time-varying HR (TVHR) can be obtained. Methods: A TVHR approach was applied to a meta-analysis of chemotherapy compared to Standard of Care for advanced recurrent gastric cancer, and in which Progression-Free Survival (PFS) was an outcome. The approach was also applied to a network meta-analysis (NMA) evaluating overall survival (OS) in advanced BRAF-mutated melanoma. Results: Five trials in the advanced gastric cancer meta-analysis displayed evidence of non-proportional hazards for PFS. Using a TVHR model produced HRs ranging from 0.83 (CrI:0.75-0.91) at 0.5 years to 0.99 (CrI:0.79-1.23) at 3.5 years. Three studies showed evidence of non-proportional hazards in the advanced BRAF-mutated melanoma NMA for OS. Using a TVHR model, nivolumab plus ipilimumab demonstrated consistent superiority from month 7 onwards, with a HR improving from 0.37 (CrI:0.26-0.51) at one year to 0.24 (CrI:0.12-0.45) at five years. Conclusions: A TVHR approach to the meta-analysis or NMA of TTE outcomes when the proportional hazards assumption appears not to hold, produces an intuitive solution which can be readily used in HTA.

Methodological guidelines for real-world evidence (RWE) in European Union (EU) joint clinical assessments (JCA) are lacking. This manuscript explores RWE potential in EU health technology assessment (HTA) and offers recommendations for generating high-quality RWE. An environmental scan of peer-reviewed and gray literature was conducted to review RWE frameworks and documents in EU regulatory and HTA decision-making. Extraction elements were standardized across key RWE themes: data quality, methodological rigor, stakeholder engagement, and applications. In JCA, RWE has multiple uses, including informing PICO simulation exercises, understanding disease landscape, identifying prognostic factors and effect modifiers, and directly or indirectly informing comparative clinical assessments. Methodological guidance from the HTA Coordination Group is limited to cases in which evidence from non-randomized studies is used as direct inputs in comparative assessments. Individual HTA bodies provide more detailed guidance, missing an opportunity to leverage RWE within JCAs that can offer insight for local Member State submissions. Generating high-quality RWE that is credible, actionable, and acceptable for JCA submissions and local HTA bodies requires careful attention to methodological considerations and early planning. Broader RWE integration that reflects patient journeys is needed. Expanding the HTA Coordination Group guidance can unlock RWE’s full potential in supporting EU JCA submissions.

Introduction. Cost-effectiveness analyses are vital in guiding decisions on treatment reimbursement. Natural history models are central to these, enabling the estimation of long-term costs and quality-adjusted life-years (QALYs) in the absence of lifetime trial data. Rare disease data are often scarce, resulting in disease progression being estimated through clinical assumptions. This study aims to evaluate how different modeling approaches influence cost-effectiveness estimates in rare disease health technology assessments (HTAs), using Duchenne muscular dystrophy (DMD) as a case study. Methods. A published economic model was used to compare 2 approaches for estimating disease progression: an assumption-based method relying on clinical plausibility and data-driven methods using data from 1,005 patients with DMD across 8 studies. Transition probabilities were estimated assuming increasing flexibility of study heterogeneity and compared with a simulated treatment cohort. Models were evaluated by comparing incremental cost-effectiveness ratios (ICERs) across approaches. No gold standard exists, so the plausibility of predictions was evaluated by comparing survival and disease progression estimates to published milestones. Results. Results showed that although the assumption-based model was clinically plausible, it predicted higher QALY gains (0.77) and lower ICERs (£1.96M per QALY) than data-driven methods did, which estimated QALY gains of 0.25, 0.26, 0.27, and 0.28 and ICERs of £6.2M, £6.2M, £5.8M, and £5.7M per QALY for the least to most flexible models, respectively. Limitations. No covariate effects or updated cost and utility data were incorporated, as the study purpose was a methodological comparison between approaches. Analyses were deterministic not probabilistic. Conclusions and Implications. This study emphasizes the critical role of model selection for HTA in rare diseases, showing that cost-effectiveness estimates from robust data-driven approaches can differ from clinically plausible assumption-based models. Highlights The choice of a natural history modeling method can drastically alter the cost-effectiveness results in rare disease evaluations. A case study in Duchenne muscular dystrophy demonstrates how different modeling approaches yield divergent cost-effectiveness outcomes. Assumption-based models, even when clinically plausible, may underestimate measures of cost-effectiveness and result in less reliable guidance for decision makers. Data-driven models using real-world patient data provide more reliable estimates for health technology assessment (HTA). This study offers practical guidance for analysts and HTA bodies on selecting robust modeling approaches in rare disease contexts.

Introduction Obesity affects over a quarter of the UK population and can lead to serious health issues. NHS Specialist Weight Management Services (WMS) offer treatments including lifestyle advice, psychological support and medications, but access and availability vary by region. Although around 4 million people could be eligible for NHS Specialist WMS annually, capacity is limited to 35 000, severely limiting overall access for those who need it. While digital technology has started to be used in WMS, more evidence is needed to confirm its long-term effectiveness, acceptability and cost-effectiveness. This study explores the use of Gro Health W8Buddy, a digital platform and app providing remote Specialist WMS. It aims to determine the long-term health benefits of remote WMS pathway Gro Health W8Buddy compared with standard NHS WMS delivered in hospitals, and to improve patients access to services.Methods and analysis The study is a real-world evaluation with observational data collection. We will recruit 450 study participants from four NHS specialist WMS who will choose either standard NHS WMS or the digital pathway Gro Health W8Buddy. Participants are being given the option to choose their pathway to generate real-world evidence. We will measure and analyse health outcomes including weight loss, time taken to be treated and cost-effectiveness, at 18 months and follow up at 24 months for later analysis (outside of this core funding). We will gather experiential data from patients and healthcare professionals through surveys, observation and interviews.Ethics and dissemination Ethical approval has been obtained from NHS Health Research Authority (HRA) and Health and Care Research Wales (HCRW) (Supplementary Figure 3) (REC reference: 25/EM/0147). Our findings will be disseminated through academic publications, conference presentations and stakeholder engagement.Trial registration ISRCTN89168871; Pre-results.

Multiple long-term conditions (MLTC) are increasingly observed in clinical practice globally. Clustering methods to group diseases into commonly co-occurring clusters have been of interest for further understanding of how MLTC group together and their associated impact on patient outcomes. However, such approaches require large, often population-scale datasets. Bayesian Profile Regression (BPR) is a statistical model that combines a Dirichlet Process Mixture model with a hierarchical regression model, in order to form clusters of items conditional on covariates and an outcome of interest. We developed a BPR model using full-rank Stochastic Variational Inference (SVI) for application in large-scale data. We assessed it's performance using simulation studies comparing fits using the No-U-turn (NUTS) sampler and full-rank SVI. We then fit a BPR model to find clusters of MLTC in a population-scale data held in the Secure Anonymised Information Linkage (SAIL) databank. We found results from full-rank SVI compared well with results from NUTS in a simulation study, and the improved fitting performance allowed for fitting models in population-scale datasets. There were 1,296,463 individuals in our electronic health record (EHR) cohort. The clustering model was conditioned on age at cohort entry, socioeconomic deprivation and sex with mortality as the outcome. We used the Elixhauser comorbidity index disease definitions, and found there were 33 disease clusters. We found that clusters featuring metastatic cancer and cardiovascular diseases, such as congestive heart failure, were most strongly associated with the probability of mortality. Our findings show that SVI can be a useful and accurate method for fitting Bayesian models, especially when the dataset size would make Monte Carlo methods prohibitively time consuming or impossible.

Meta-analyses of time-to-event (TTE) outcomes, particularly in Health Technology Assessment (HTA), commonly use a hazard ratio (HR) scale. However, non-proportional hazards in included trials create difficulties. Existing methods are either too complex or not easily incorporated into economic decision models for cost-effectiveness assessment. An alternative approach assumes a treatment-log(time) interaction within a Cox proportional hazards model, allowing the log HR to vary linearly with log(time). A bivariate meta-analysis of the resulting treatment and interaction coefficients then yields an overall time-varying HR (TVHR) with appropriate uncertainty. The TVHR approach was applied to a meta-analysis of 20 trials (4,069 patients) comparing chemotherapy to Standard of Care (SoC) for advanced recurrent gastric cancer, with Progression-Free Survival (PFS) as an outcome (median follow-up 1.2 years). It was also applied to a network meta-analysis (NMA) of 13 treatments across 13 Randomised Controlled Trials (RCTs) in previously untreated advanced BRAF-mutated melanoma (3,913 deaths in 6,378 participants) evaluating Overall Survival (OS). Both applications were compared against standard Bayesian meta-analysis assuming proportional hazards. Five trials in the gastric cancer meta-analysis showed non-proportional hazards for PFS. A standard Bayesian random-effects meta-analysis yielded a pooled HR of 0.78 (95

BACKGROUND:Overall survival (OS) is the standard efficacy endpoint in various solid tumor trials; however, it requires longer follow-up time for assessment than potential intermediate endpoints. This study evaluated radiological progression-free survival (rPFS) as a surrogate for OS in metastatic hormone-sensitive prostate cancer (mHSPC) using aggregate-level data from randomized controlled trials (RCTs). METHODS:A systematic literature review identified mHSPC RCTs published through December 2023, reporting hazard ratios for rPFS (HRrPFS) and OS (HROS). Correlation between HRrPFS and HROS was assessed using bivariate random-effects meta-analysis (BRMA). Predictive validity was assessed with leave-one-out cross-validation (LOOCV). The surrogate threshold effect (STE), or minimum rPFS benefit predicting an OS benefit, was estimated using recent mHSPC trial sample sizes. Sensitivity analyses (1) omitted trials that had only one of the endpoints reported, (2) omitted HRs that violated proportional hazards assumptions, (3) omitted trials that allowed cross-over and (4) investigated different assumed values of the within-study correlation. RESULTS:The primary analysis included 35 treatment comparisons from 31 trials. The estimated rPFS-OS correlation was 0.95 (95 % CrI: 0.75, 1.00). LOOCV confirmed HROS were within 95 % prediction intervals. The estimated STE ranged from 0.55 to 0.71 depending on the trial size being predicted. Sensitivity analyses produced strong but slightly lower correlations (0.87, 0.89, 0.91) than the primary analysis, with full coverage of the reported HROS in cross validation. Increasing within-study correlation slightly reduced between-study correlation. CONCLUSIONS:The derived surrogacy equation enables OS estimation based on reported rPFS benefits in mHSPC, meeting NICE's 95 % surrogate validity threshold. These findings support rPFS as a reliable surrogate for OS, facilitating prediction of OS benefits in future mHSPC trials.

Despite the proven efficacy of androgen deprivation therapy (ADT) combined with androgen receptor pathway inhibitors (ARPIs) in metastatic castration-sensitive prostate cancer (mCSPC), many patients still receive ADT monotherapy due to safety concerns. This reliance on ADT monotherapy underscores the need for education on the comparative effectiveness and safety of available therapies versus ADT. We evaluated the efficacy and safety of alternative treatment combinations, incorporating final data from the recent ARANOTE Phase III trial. We conducted network meta-analysis (NMA) to evaluate progression-free survival (PFS) and overall survival (OS), incorporating heterogeneity assessment through subgroup analyses. Additionally, we performed a separate class effect NMA. We analysed grade 3-5 adverse events (AEs), serious AEs, and discontinuation due to AEs. We estimated hazard ratios (HRs) for efficacy, rate ratios (RRs) for safety, 95

Objectives To investigate the use of a Bayesian joint modelling approach to predict overall survival (OS) from immature clinical trial data using an intermediate biomarker. To compare the results with a typical parametric approach of extrapolation and observed survival from a later datacut. Methods Data were pooled from three phase I/II open-label trials evaluating larotrectinib in 196 patients with neurotrophic tyrosine receptor kinase fusion-positive (NTRK+) solid tumours followed up until July 2021. Bayesian joint modelling was used to obtain patient-specific predictions of OS using individual-level sum of diameter of target lesions (SLD) profiles up to the time at which the patient died or was censored. Overall and tumour site-specific estimates were produced, assuming a common, exchangeable, or independent association structure across tumour sites. Results The overall risk of mortality was 9 Conclusions Joint modelling using intermediate outcomes such as tumour burden can offer an alternative approach to traditional survival modelling and may improve survival predictions from limited follow-up data. This approach allows complex hierarchical data structures, such as patients nested within tumour types, and can also incorporate multiple longitudinal biomarkers in a multivariate modelling framework.

Surrogate endpoints are used when the primary outcome is difficult to measure accurately. Determining if a measure is suitable to use as a surrogate endpoint is a challenging task and a variety of meta-analysis models have been proposed for this purpose. The Daniels and Hughes bivariate model for trial-level surrogate endpoint evaluation is gaining traction but presents difficulties for frequentist estimation and hitherto only Bayesian solutions have been available. This is because the marginal model is not a conventional linear model and the number of unknown parameters increases at the same rate as the number of studies. This second property raises immediate concerns that the maximum likelihood estimator of the model's unknown variance component may be downwardly biased. We derive maximum likelihood estimating equations to motivate a bias adjusted estimator of this parameter. The bias correction terms in our proposed estimating equation are easily computed and have an intuitively appealing algebraic form. A simulation study is performed to illustrate how this estimator overcomes the difficulties associated with maximum likelihood estimation. We illustrate our methods using two contrasting examples from oncology.

Aim: The use of amyloid-beta (Aβ) clearance to support regulatory approvals of drugs in Alzheimer's disease (AD) remains controversial. We evaluate Aβ as a potential trial-level surrogate endpoint for clinical function in AD. Materials & methods: Data on the effectiveness of anti-Aβ monoclonal antibodies (MABs) on Aβ and multiple clinical outcomes were identified from randomized controlled trials through a literature review. A Bayesian bivariate meta-analysis was used to evaluate Aβ as a surrogate endpoint for clinical function across all MABs and for each individual anti-Aβ MAB. The analysis for individual therapies was conducted in subgroups of treatments and by applying Bayesian hierarchical models to borrow information across treatments. Results: We identified 23 randomized controlled trials with 39 treatment contrasts for seven MABs. The surrogate relationship between treatment effects on Aβ and Clinical Dementia Rating-Sum of Boxes (CDR-SOB) across all MABs was strong: with a meaningful slope of 1.41 (0.60, 2.21) and small variance of 0.02 (0.00, 0.05). For individual treatments, the surrogate relationships were suboptimal, displaying large uncertainty. Sharing information across treatments considerably reduced the uncertainty, resulting in moderate surrogate relationships for aducanumab and lecanemab. No meaningful association was detected for other clinical outcomes, including Mini Mental State Examination and Alzheimer's Disease Assessment Scale-Cognitive Subscale. Conclusion: Although our results from the analysis of data across all MABs suggested that Aβ was a potential surrogate endpoint for CDR-SOB, individually the surrogacy patterns varied across treatments and showed no evidence of association. Bayesian information-sharing revealed moderate surrogate relationship only for aducanumab and lecanemab.

The aim of this study was to pool multiple data sets to build a patient-centric, data-informed, natural history model (NHM) for Duchenne muscular dystrophy (DMD) to estimate disease trajectory across patient lifetime under current standard of care in future economic evaluations. The study was conducted as part of Project HERCULES, a multi-stakeholder collaboration to develop tools to support health technology assessments of new treatments for DMD. Health states were informed by a review of NHMs for DMD and input from clinicians, patients and caregivers, and defined using common outcomes in clinical trials and real-world practice. The primary source informing the NHM was the Critical Path Institute Duchenne Regulatory Science Consortium (D-RSC) database. This was supplemented with expert input obtained via an elicitation exercise, and a systematic literature review and meta-analysis of mortality data. The NHM includes ambulatory, transfer and non-ambulatory phases, which capture loss of ambulation, ability to weight bear and upper body and respiratory function, respectively. The NHM estimates patients spend approximately 9.5 years in ambulatory states, 1.5 years in the transfer state and the remainder of their lives in non-ambulatory states. Median predicted survival is 34.8 years (95

Background and ObjectivesClinical trials in Duchenne muscular dystrophy (DMD) require 3-6 months of stable glucocorticoids, and the primary outcome is explored at 48-52 weeks. The factors that influence the clinical outcome assessment (COA) trajectories soon after glucocorticoid initiation are relevant for the design and analysis of clinical trials of novel drugs. We describe early COA trajectories, associated factors, and the time from glucocorticoid initiation to COA peak. MethodsThis was a prospective 18-month analysis of the Finding the Optimum Corticosteroid Regimen for Duchenne Muscular Dystrophy study. Four COAs were investigated: rise from supine velocity (RFV), 10-meter walk/run velocity (10MWRV), North Star Ambulatory Assessment (NSAA) total score, and 6-minute walk test distance (6MWT). The relationships of baseline age (4-5 vs 6-7 years), COA baseline performance, genotype, and glucocorticoid regimen (daily vs intermittent) with the COA trajectories were evaluated using linear mixed-effects models. ResultsOne hundred ninety-six glucocorticoid-na & iuml;ve boys with DMD aged 4-7 years were enrolled. The mean age at baseline was 5.9 +/- 1.0 years, 66% (n = 130) were on daily regimens, 55% (n = 107) showed a 6MWT distance >330 metres; 41% (n = 78) showed RFV >0.2 rise/s; 76% (n = 149) showed 10MWRV >0.142 10m/s, and 41.0% (n = 79) showed NSAA total score >22 points. Mean COA trajectories differed by age at glucocorticoid initiation (p < 0.01 for RFV, 10MWRV, and NSAA; p < 0.05 for 6MWT) and regimen (p < 0.01 for RFV, 10MWRV, and NSAA). Boys younger than 6 years reached their peak performance 12-18 months after glucocorticoid initiation. Boys aged 6 years or older on a daily regimen peaked between months 9 and 12 and those on an intermittent regimen by 9 months. The baseline COA performance was associated with the NSAA (p < 0.01) and the 6MWT trajectory in boys younger than 6 years on a daily regimen (p < 0.01). Differences in the mean trajectories by genotype were not significant. DiscussionGlucocorticoid regimen, age, duration of glucocorticoid exposure, and baseline COA performance need to be considered in the design and analysis of clinical trials in young boys with DMD.

Aim: Comparative effectiveness research (CER) is essential for making informed decisions about drug access. It provides insights into the effectiveness and safety of new drugs compared with existing treatments, thereby guiding better healthcare decisions and ensuring that new therapies meet the real-world needs of patients and healthcare systems. Objective: To provide a tool that assists analysts and decision-makers in identifying the most suitable analytical approach for answering a CER question, given specific data availability contexts. Methods: A systematic literature review of the scientific literature was performed and existing regulatory and health technology assessment (HTA) guidance were evaluated to identify and compare recommendations and best practices. Based on this review a methods flowchart that synthesizes current practices and requirements was proposed. Results: The review did not find any papers that clearly identified the most appropriate analytical approach for answering CER questions under various conditions. Therefore, a methods flowchart was designed to inform analyst and decision makers choices starting from a well-defined scientific question. Conclusion: The proposed methods flowchart offers clear guidance on CER methodologies across a range of settings and research needs. It begins with a well-defined research question and considers multiple feasibility aspects related to CER. This tool aims to standardize methods, ensure rigorous and consistent research quality and promote a culture of evidence-based decision-making in healthcare.

IntroductionIn health technology assessment (HTA), economic evaluations assessing biologic drugs for rheumatoid arthritis (RA) involve modeling patients’ responses to multiple treatments given sequentially over a lifetime horizon. When data from randomized controlled trials (RCTs) are scarce, data from non-randomized studies (e.g., single-arm trials [SATs] and disease registries) can be used to supplement the evidence base. This research aimed to demonstrate meta-analytic methods for combining effectiveness data from randomized and non-randomized studies and their corresponding impact on cost-effectiveness estimates.MethodsData comparing patients receiving second-line rituximab with continued background non-biologic treatment were extracted from one RCT and six SATs identified in an HTA assessing second-line rituximab for RA, and from the British Society for Rheumatology Biologics Register-Rheumatoid Arthritis, by applying a target trial emulation approach. A binomial meta-analysis model was used to estimate the probabilities of achieving the European League against Rheumatism (EULAR) response criteria by pooling data from the RCT, SATs, and the registry. The probabilities were entered into a decision model from a previous HTA to derive incremental cost-effectiveness ratio (ICER) estimates for treatment strategies with and without biologic drugs.ResultsCompared with the original analysis, the estimated probability of at least a moderate EULAR response on rituximab from combined sources was substantially lower. For example, the probability obtained from an RCT was 0.68 (95% credible interval [CrI]: 0.345, 0.907), but only 0.29 (95% [CrI]: 0.242, 0.333) when using RCT plus registry data and 0.29 (95% CrI: 0.244, 0.336) for combined RCT, registry, and SAT data. In the cost-effectiveness analysis, the median ICERs were higher when including real-world data.ConclusionsSynthesis of all relevant data, including RWD, provides additional information regarding the variability in cost-effectiveness estimates and can be considered in sensitivity analyses for HTA decision-making.