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Aim: Late-onset Pompe disease is characterized by progressive loss of muscular and respiratory function. Until recently, standard of care was enzyme replacement therapy (ERT) with alglucosidase alfa. Second-generation ERTs avalglucosidase alfa (aval) and cipaglucosidase alfa with miglustat (cipa+mig) are now available. Without head-to-head trials comparing aval with cipa+mig, an indirect treatment comparison is informative and timely for understanding potential clinical differentiation. Materials & methods: A systematic literature review was performed to identify relevant studies on cipa+mig and aval. Using patient-level and aggregate published data from randomized controlled trials (RCTs) and phase I/II and open-label extension (OLE) trials, a multi-level network meta-regression was conducted, adjusting for various baseline covariates, including previous ERT duration, to obtain relative effect estimates on 6-minute walk distance (6MWD, meters [m]) and forced vital capacity (FVC, % predicted [pp]). Analyses of two networks were conducted: Network A, including only RCTs, and network B, additionally including single-arm OLE and phase I/II studies. Results: Network B (full evidence analysis) showed that cipa+mig was associated with a relative increase in 6MWD (mean difference 28.93 m, 95% credible interval [8.26-50.11 m]; Bayesian probability 99.7%) and FVC (2.88 pp [1.07-4.71 pp]; >99.9%) compared with aval. The comparison between cipa+mig and aval became more favorable for cipa+mig with increasing previous ERT duration for both end points. Analysis of network A showed that cipa+mig was associated with a relative decrease in 6MWD (-10.02 m [-23.62 to 4.00 m]; 91.8%) and FVC (-1.45 pp [-3.01 to 0.07 pp]; 96.8%) compared with aval. Conclusion: Cipa+mig showed a favorable effect versus aval when all available evidence was used in the analysis.

Background and objectives: The National Institute for Health and Care Excellence (England's health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However there is limited literature on methods to estimate the ATE, particularly in the case of survival outcomes. Single-arm trials and real-world data are playing an increasing role in health technology assessments, particularly in oncology/rare diseases, generating a need for new ATE estimation methods. This study aimed to present the adaptation and utility of this methodology for survival outcomes. Methods: The approach is based on a "doubly robust" method combining matching with regression adjustment (Austin 2020) using a Weibull model (lowest Akaike information criteria [AIC] specification) to estimate counterfactual event times. As a case study, we compared mosunetuzumab versus rituximab/bendamustine, as a proxy for rituximab/chemotherapy, in 3L+ relapsed/refractory follicular lymphoma. Individual patient data for mosunetuzumab (NCT02500407) and a combination of two rituximab/bendamustine 3L+ follicular lymphoma cohorts (NCT02187861/NCT02257567) were used. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test robustness to different distributional assumptions (log-normal, log-logistic and exponential) or model specifications (second, third and fourth lowest AIC) for event times. Results: The case study found improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.13, 0.91]) and OS (HR 0.30 [95% CI: 0.05, 5.28]) for mosunetuzumab. Consistent findings (HR range 0.25–0.47 and 0.21–0.50 with all CIs excluding/including 1 for PFS/OS, respectively) were observed in sensitivity analyses. Discussion/conclusions: The proposed adaptation expands the range of available approaches for the estimation of the (local) ATE for survival outcomes in health technology assessments using "doubly robust" methods. This approach appeared relatively robust to modelling decisions in our case study. [ABSTRACT FROM AUTHOR]

Background: Population-adjusted indirect comparison using parametric Simulated Treatment Comparison (STC) has had limited application to survival outcomes in unanchored settings. Matching-Adjusted Indirect Comparison (MAIC) is commonly used but does not account for violation of proportional hazards or enable extrapolations of survival. We developed and applied a novel methodology for STC in unanchored settings. We compared overall survival (OS) and progression-free survival (PFS) of lenvatinib plus pembrolizumab (LEN + PEM) against nivolumab plus ipilimumab (NIVO + IPI), pembrolizumab plus axitinib (PEM + AXI), avelumab plus axitinib (AVE + AXI), and nivolumab plus cabozontanib (NIVO + CABO) in patients with advanced renal cell carcinoma (RCC). Unanchored comparison was necessitated as the control groups differed in their use of PD-1/PD-L1 rescue therapy. Methods: We fit covariate-adjusted survival models to individual patient data from phase 3 trial of LEN + PEM, including standard parametric distributions and Royston-Parmar spline models with up to 3 knots. We used these models to predict OS and PFS in the population of comparator treatments. The base case model was selected by minimum Akaike Information Criterion (AIC). Treatment effects were measured using difference in restricted mean survival time (RMST), over shortest follow-up of input trials, and hazard ratios at 6, 12, 18, and 24 months. Results: The survival model with the lowest AIC was 1-knot spline odds for OS and log-logistic for PFS. Difference in RMST OS was 6.90 months (95% CI: 1.95, 11.36), 5.31 (3.58, 7.28), 5.99 (1.82, 9.42), and 11.59 (8.41, 15.38) versus NIVO + IPI (over 64.8 months follow-up), AVE + AXI (46.7 months), PEM + AXI (64.8 months), NIVO + CABO (53.0 months), respectively. Difference in RMST PFS was 4.50 months (95% CI: 0.92, 8.26), 8.23 (5.60, 10.57), 5.38 (2.06, 9.09), and 4.58 (0.09, 9.44) versus NIVO + IPI (over 57.8 months), AVE + AXI (44.9 months), PEM + AXI (57.8 months), NIVO + CABO (23.8 months), respectively. Hazard ratios indicated strong evidence of greater OS and PFS on LEN + PEM at most timepoints. Conclusions: We developed and applied a novel methodology for comparing survival outcomes in unanchored settings using STC. Pending investigation with a simulation study or further examples, this methodology could be used for clinical decision-making and, if long-term data are available, inform economic models designed to extrapolate outcomes for the evaluation of lifetime cost-effectiveness. Trial registration: NCT02811861 (registered: 23/06/2016). [ABSTRACT FROM AUTHOR]

This paper considers the problem in aggregate data meta-analysis of studies reporting multiple competing binary outcomes and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared parameter model on hazard ratio scale accounting for different data summaries and competing risks. We adapt theoretical arguments from the literature to demonstrate that the models are equivalent if events are rare. We use constructed data examples and a simulation study to find an event rate threshold of approximately 0.2 above which competing risks and different data summaries may bias results if no adjustments are made. Below this threshold, simpler models may be sufficient. We recommend analysts to consider the absolute event rates and only use a simple model ignoring data types and competing risks if all of underlying events are rare (below our threshold of approximately 0.2). If one or more of the absolute event rates approaches or exceeds our informal threshold, it may be necessary to account for data types and competing risks through a shared parameter model in order to avoid biased estimates.

Network meta-analysis compares multiple treatments from studies that form a connected network of evidence. However, for complex networks, it is not easy to see if the network is connected. We use simple techniques from graph theory to test the connectedness of evidence networks in network meta-analysis. The method is to build the adjacency matrix for a network, with rows and columns corresponding to the treatments in the network and entries being one or zero depending on whether the treatments have been compared or not, and with zeros along the diagonal. Manipulation of this matrix gives the indirect connection matrix. The entries of this matrix determine whether two treatments can be compared, directly or indirectly. We also describe the distance matrix, which gives the minimum number of steps in the network required to compare a pair of treatments. This is a useful assessment of an indirect comparison as each additional step requires further assumptions of homogeneity in, for example, design and target populations of included trials. If there are no loops in the network, the distance is a measure of the degree of assumptions needed; it is approximately this with loops. We illustrate our methods using several constructed examples and giving R code for computation. We have also implemented the techniques in the Stata package "network." The methods provide a fast way to ensure comparisons are only made between connected treatments and to assess the degree of indirectness of a comparison.

Author(s): Emma Rose McGlone 1,2, Iain Carey 3, Vladica Velickovic 4, Prem Chana 1, Kamal Mahawar 1, Rachel L. Batterham 1,5,6, James Hopkins 1, Peter Walton 1, Robin Kinsman 1, [...], Background Although bariatric surgery is well established as an effective treatment for patients with obesity and type 2 diabetes mellitus (T2DM), there exists reluctance to increase its availability for patients with severe T2DM. The aims of this study were to examine the impact of bariatric surgery on T2DM resolution in patients with obesity and T2DM requiring insulin (T2DM-Ins) using data from a national database and to develop a health economic model to evaluate the cost-effectiveness of surgery in this cohort when compared to best medical treatment (BMT). Methods and findings Clinical data from the National Bariatric Surgical Registry (NBSR), a comprehensive database of bariatric surgery in the United Kingdom, were extracted to analyse outcomes of patients with obesity and T2DM-Ins who underwent primary bariatric surgery between 2009 and 2017. Outcomes for this group were combined with data sourced from a comprehensive literature review in order to develop a state-transition microsimulation model to evaluate cost-effectiveness of bariatric surgery versus BMT for patients over a 5-year time horizon. The main outcome measure for the clinical study was insulin cessation at 1-year post-surgery: relative risks (RR) summarising predictive factors were determined, unadjusted, and after adjusting for variables including age, initial body mass index (BMI), duration of T2DM, and weight loss. Main outcome measures for the economic evaluation were total costs, total quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER) at willingness-to-pay threshold of GBP£20,000. A total of 2,484 patients were eligible for inclusion, of which 1,847 had 1-year follow-up data (mean age of 51 years, mean initial BMI 47.2 kg/m.sup.2, and 64% female). 67% of patients no longer required insulin at 1-year postoperatively: these rates persisted for 4 years. Roux-en-Y gastric bypass (RYGB) was associated with a higher rate of insulin cessation (71.7%) than sleeve gastrectomy (SG; 64.5%; RR 0.92, confidence interval (CI) 0.86-0.99) and adjustable gastric band (AGB; 33.6%; RR 0.45, CI 0.34-0.60; p < 0.001). When adjusted for percentage total weight loss and demographic variables, insulin cessation following surgery was comparable for RYGB and SG (RR 0.97, CI 0.90-1.04), with AGB having the lowest cessation rates (RR 0.55, CI 0.40-0.74; p < 0.001). Over 5 years, bariatric surgery was cost saving compared to BMT (total cost GBP£22,057 versus GBP£26,286 respectively, incremental difference GBP£4,229). This was due to lower treatment costs as well as reduced diabetes-related complications costs and increased health benefits. Limitations of this study include loss to follow-up of patients within the NBSR dataset and that the time horizon for the economic analysis is limited to 5 years. In addition, the study reflects current medical and surgical treatment regimens for this cohort of patients, which may change. Conclusions In this study, we observed that in patients with obesity and T2DM-Ins, bariatric surgery was associated with high rates of postoperative cessation of insulin therapy, which is, in turn, a major driver of overall reductions in direct healthcare cost. Our findings suggest that a strategy utilising bariatric surgery for patients with obesity and T2DM-Ins is cost saving to the national healthcare provider (National Health Service (NHS)) over a 5-year time horizon.

Introduction: Most patients with long-term conditions (LTC) receive regular blood tests to monitor disease progression and response to treatment and to detect complications. There is currently no robust evidence to inform recommendations on monitoring. Creating this evidence base is challenging because the benefits and harms of testing are dependent on what is done in response to the test results. Methods: We identified a list of commonly used tests. We defined a series of filtering questions to determine whether there was evidence to support the rationale of monitoring, such as "Can the general practitioner do anything in response to an abnormal test result?" Through a series of rapid reviews we identified evidence to answer each question. The evidence was presented at a consensus meeting where clinicians and patients voted for inclusion, exclusion, or further analysis. A process evaluation was performed alongside this. Further analyses were performed using routinely collected healthcare data and by performing incidence analyses, emulating randomized controlled trials (RCTs), and modeling disease progression. Results: We tested this methodology on three common LTCs: chronic kidney disease (CKD), type 2 diabetes mellitus (T2DM), and hypertension. We found sufficient evidence to include hemoglobin A1C and estimated glomerular filtration rate (eGFR) for monitoring patients with T2DM; hemoglobin and eGFR for patients with CKD; and eGFR for patients with hypertension. The consensus panel excluded four tests, while 10 tests were selected for further analysis. The emulated RCTs will investigate the effect of regular monitoring with certain tests on health outcomes among routinely monitored patients. In addition, we will investigate the signal-to-noise ratio of each test over time using a modeling approach. Conclusions: The cost effectiveness of the evidence-based testing panels needs to be tested in clinical practice. We are currently developing an intervention package and are planning to run a feasibility trial. This program of work has the potential to change how LTCs are monitored in primary care, ultimately improving patient outcomes and leading to more efficient use of healthcare resources. [ABSTRACT FROM AUTHOR]