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Andrea Gabrio,1 Necdet B Gunsoy,2 Gianluca Baio,1 Alan Martin,3 Victoria F Paly,4 Nancy Risebrough,5 David MG Halpin,6 Dave Singh,7 Robert A Wise,8 MeiLan K Han,9 Fernando J Martinez,10 Gerard J Criner,11 Neil Martin,12 David A Lipson,13,14 Afisi S Ismaila15,16 1UCL Statistical Science, University College London, London, UK; 2Value Evidence & Outcomes, GlaxoSmithKline, Brentford, UK; 3Value Evidence and Outcomes, GlaxoSmithKline, Uxbridge, UK; 4Global HTA, Health Economics, Reimbursement & Outcomes, ICON Plc., Philadelphia, PA, USA; 5Global HTA, Health Economics, Reimbursement & Outcomes, ICON plc., Toronto, ON, Canada; 6University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK; 7The Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, Manchester Academic Health Science Centre, University of Manchester, Manchester University NHS Foundation Trust, Manchester, UK; 8The Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; 9University of Michigan, Pulmonary & Critical Care, Ann Arbor, MI, USA; 10Joan and Sandy Weill Department of Medicine, New York-Presbyterian Hospital/Weill Cornell Medical Center, New York, NY, USA; 11Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA; 12Global Medical Affairs, GlaxoSmithKline, Uxbridge, UK; 13Development Clinical Sciences, GlaxoSmithKline, Collegeville, PA, USA; 14Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 15Value Evidence and Outcomes, GlaxoSmithKline, Collegeville, PA, USA; 16Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, CanadaCorrespondence: Afisi S Ismaila, Value Evidence and Outcomes, GlaxoSmithKline, 1250 S. Collegeville Road, Collegeville, PA, 19426-0989, USA, Tel +19199320430; +19193158229, Email afisi.s.ismaila@gsk.comAbstract: Objectives: In the IMPACT trial (NCT02164513), triple therapy with fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) showed clinical benefit compared with dual therapy with either FF/VI or UMEC/VI in the treatment of chronic obstructive pulmonary disease (COPD). We used data from IMPACT to determine whether this translated into differences in COPD-related healthcare resource utilization (HRU) costs in a United Kingdom (UK) setting.Methods: In a within-trial analysis, individual patient data from the IMPACT intention-to-treat (ITT) population were analyzed to estimate rates of COPD-related HRU with FF/UMEC/VI, FF/VI, or UMEC/VI. A Bayesian approach was applied to address issues typically encountered with this kind of data, namely data missing due to early study withdrawal, subjects with zero reported HRU, and skewness. Rates of HRU were estimated under alternate assumptions of data being missing at random (MAR) or missing not at random (MNAR). UK-specific unit costs were then applied to estimated HRU rates to calculate treatment-specific costs.Results: Under each MNAR scenario, per patient per year (PPPY) rates of COPD-related HRU were lowest amongst those patients who received treatment with FF/UMEC/VI compared with those receiving either FF/VI or UMEC/VI. Although absolute HRU rates and costs were typically higher for all treatment groups under MNAR scenarios versus MAR, final economic conclusions were robust to patient withdrawals.Conclusions: PPPY rates were typically lower with FF/UMEC/VI versus FF/VI or UMEC/VI.Keywords: single-inhaler triple therapy, COPD, healthcare resource use, cost

Irene Petersen,1,2 Sia Kromann Nicolaisen,2 Federico Ricciardi,3 Manuj Sharma,1 Reimar W Thomsen,2 Gianluca Baio,3 Lars Pedersen2 1Department of Primary Care and Population Health, University College London, London, UK; 2Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 3Department of Statistical Science, University College London, London, UKCorrespondence: Irene PetersenDepartment of Primary Care and Population Health, University College London, London NW3 2PF, UKTel +44 20 801 68032Email i.petersen@ucl.ac.ukBackground: Individuals with type 2 diabetes (T2D) have a twofold increased risk for cardiovascular events (CVE), and CVE is responsible for nearly 80% of the mortality. Current treatment guidelines state that individuals should immediately initiate antidiabetic treatment and cardiovascular risk-factor management from T2D diagnosis. However, the evidence base is sparse, and randomized trials are unlikely to be conducted. We examined the impact of being eligible for T2D treatment, as determined by the threshold of HbA1c ≥ 6.5% (≥ 48 mmol/mol), on all-cause mortality and CVE. We hypothesised that individuals who were just above this threshold had a lower risk of CVE and all-cause mortality than individuals just below.Methods and Findings: We used the regression discontinuity design (RDD), a quasi-experimental design, comparing rates of all-cause mortality and CVE in people just below and just above the eligibility for treatment threshold. We included Danish healthcare records from 43,070 individuals aged 40– 80 years with no previous T2D record and the first record of HbA1c in the range of 6.0– 7.0% (42– 53 mmol/mol) between 2006 and 2014. In total, 36,360 individuals had the first record of HbA1c between 6.0% and 6.4% (42– 47 mmol/mol), and 6710 individuals had a first record between 6.5% and 7.0% (48– 53 mmol/mol). Individuals with a measurement just above 6.5% (48 mmol/mol) had a 21% lower rate of death or CVE, compared to those just below (hazard ratio: 0.79 (95% CI 0.69– 0.90)). Few individuals received early metformin treatment. However, the chance of metformin treatment initiation within 3 months was substantially higher for individuals with an HbA1c measurement above (14%) than below (1%) the threshold.Conclusion: Individuals with first record of HbA1c measure just above treatment threshold experienced a 21% lower rate of death or CVE than those just below. Lifestyle modifications and cardiovascular risk-factor management may contribute to this reduced rate.Keywords: type 2 diabetes, glycated hemoglobin A1c, regression discontinuity design, cardiovascular event, mortality

Objectives: Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non–small-cell lung cancer (NSCLC). Methods: Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs—landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)—were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. Results: For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). Conclusions: We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Highlights: Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up. Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes. Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data. Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting. Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup. [ABSTRACT FROM AUTHOR]

Research describing the effect of annealing temperature on transition metal perovskites is presented. In particular the existence of an electronic phase separation characteristic within the material is investigated.