Use Of Real-world Data Research Articles

The Use of Real-World Data for Estimating Relative Treatment Effects in NICE Health Technology Assessment Submissions: A Review.

This paper investigates the current use of real-world data (RWD) for estimating relative treatment effects in National Institute for Health and Care Excellence (NICE) health technology assessment (HTA) submissions. This review included 64 HTA submissions, which accounted for approximately 11% of the total NICE HTA submissions between January 2016 and December 2023. The main sources of RWD considered in the submissions were disease registries and electronic health records. RWD were primarily used to provide an external control arm to enable comparisons within single-arm trials and to inform long-term treatment effects when extrapolating survival data beyond the trial follow-up. Adjustments for potential systematic differences between treatment groups have improved over time; however, approximately one-third of the submissions still relied on unadjusted treatment comparisons. We found that approximately 10% of NICE HTA submissions used RWD to directly inform treatment effects estimation. Over one-third of the submissions relied on naïve and/or unadjusted treatment comparisons, which may have introduced biases. To ensure that RWD provide sound evidence for HTA, submissions should follow published guidelines, including the NICE real-world evidence (RWE)framework.

The arisal of data spaces: why I am excited and worried.

This paper explores the significant role of real-world data (RWD) in advancing our understanding and management of Multiple Sclerosis (MS). RWD has proven invaluable in MS research and care, offering insights from larger and diverse patient populations. A key focus of the paper is the European Health Data Space (EHDS), a significant development that promises to change how healthcare data is managed across Europe. This initiative is particularly relevant to the MS community. The paper highlights various data initiatives, discussing their importance for those affected by MS. Despite the potential benefits, there are challenges and concerns, especially about ensuring that the growth of various data platforms remains beneficial for MS patients. The paper suggests practical actions for the global MS community to consider, aimed at optimizing the use of RWD. The emphasis of this discussion is on the secondary use of health data, particularly in the European context. The content is based on the author's own experiences and interpretations, offering a personal yet informed view on using RWD to improve MS research and patient care.

Fuel Cell Electric Buses: A Systematic Literature Review

This paper presents a comprehensive review of scientific papers and market reports analyzing the economic competitiveness of fuel cell electric buses (FCEBs) with respect to their conventional alternatives via the total cost of ownership (TCO) methodology. We discussed the variables and data taken into account and compared the resulting outcomes by year and geographical areas. It emerged that FCBs are not currently cost competitive. The decreasing trend in acquisition and fuel costs, however, indicates potential for future competitiveness. We find that the current TCO literature on FCEBs presents several areas of uncertainty and weakness. Potential improvements can be achieved by: (i) extending the geographic coverage to Asian and African developing countries; (ii) making use of real-world data instead of simulated data, in particular, concerning acquisition costs, hydrogen costs under different pathways, fuel efficiency, and maintenance costs; (iii) clarifying the role of infrastructural costs; (iv) exploring the existence of economies of scale at fleet level; (v) distinguishing among different bus sizes.

Status of Awareness and Use of Real-World Data and Real-World Evidence in the Domestic Drug Approval Process: Insights from the Survey Study

Status of Awareness and Use of Real-World Data and Real-World Evidence in the Domestic Drug Approval Process: Insights from the Survey Study

Analysis of factors associated with use of real-world data in single technology appraisals of cancer drugs by the National Institute for Health and Care Excellence

ObjectivesThis study investigates factors associated with use of real-world data (RWD) in economic modelling for single technology appraisals (STAs) of cancer drugs by the National Institute for Health and Care Excellence (NICE) to improve systematic understanding of the use of RWD. MethodsThe data were extracted from STAs of cancer drugs, for which NICE issued guidance between January 2011 and December 2022 (n=267). Binary regression was used to test hypotheses concerning the greater or lesser use of RWD. Bonferroni-Holm correction was used to control error rates in multiple hypotheses tests. Several explanatory variables were considered in this analysis, including time (Time), incidence rate of disease (IR), availability of direct treatment comparison (AD), generalisability of trial data (GE), maturity of survival data in trial (MS) and previous technology recommendations by NICE (PR). The primary outcome variable was any use of RWD. Secondary outcome variables were specific uses of RWD in economic models. ResultsAD had a statistical negative association with any use of RWD whereas no associations with non-parametric and parametric use of RWD were found. Time had several statistical associations with use of RWD (validating survival distributions for the intervention, estimating progression-free survival for the intervention, estimating overall survival for comparators and transition probabilities). ConclusionsRWD were more likely to be used in economic modelling of cancer drugs when randomised controlled trials failed to provide relevant clinical information of the drug for appraisals, particularly in the absence of direct treatment comparisons. These results, based on analysis of data systematically collected from previous appraisals, suggest that uses of RWD were associated with data gaps in the economic modelling. While this result may support some of the claimed advantages of using RWD when evidence is absent, the question, the extent to which use of RWD in indirect treatment comparisons reduces uncertainty is still to be determined.

A Study of the Main Mathematical Models Used in Mobility, Storage, Pickup and Delivery in Urban Logistics: A Systematic Review

This systematic review investigates the main mathematical models applied in urban logistics, focusing on routing, location and transshipment problems. The study addresses the growing demand for efficient and sustainable logistics solutions driven by population growth and the expansion of e-commerce. A thorough analysis of 57 scientific articles was carried out, covering deterministic and stochastic methodologies, as well as heuristic and exact solutions. This review revealed that heuristic methods are predominant due to their computational efficiency. Combining exact methods with heuristics has proven effective for complex logistics scenarios, increasing accuracy and efficiency. Synchronization and intermediate stops have also emerged as critical factors in optimizing logistics operations. This review highlights the diversity of methodologies and the need for sustainable and efficient models. The integration of stochastic simulations remains limited, representing a research gap where stochastic models have been shown to provide more robust solutions in addressing the uncertainties inherent in logistics operations. This integration can increase the robustness and applicability of logistics solutions in urban environments. By highlighting the strengths and limitations of current approaches, it paves the way for future research to develop more robust and adaptable solutions to urban logistics challenges, emphasizing interdisciplinary collaboration and the use of real-world data.

Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

Lessons on the use of real-world data in medical device research: findings from the National Evaluation System for Health Technology Test-Cases.

Aim: Although the US FDA encourages manufacturers of medical devices to submit real-world evidence (RWE) to support regulatory decisions, the ability of real-world data (RWD) to generate evidence suitable for decision making remains unclear. The 2017 Medical Device User Fee Amendments (MDUFA IV), authorized the National Evaluation System for health Technology Coordinating Center (NESTcc) to conduct pilot projects, or 'Test-Cases', to assess whether current RWD captures the information needed to answer research questions proposed by industry stakeholders. We synthesized key lessons about the challenges conducting research with RWD and the strategies used by research teams to enhance their ability to generate evidence from RWD based on 18 Test-Cases conducted between 2020 and 2022. Materials & methods: We reviewed study protocols and reports from each Test-Case team and conducted 49 semi-structured interviews with representatives of participating organizations. Interview transcripts were coded and thematically analyzed. Results: Challenges that stakeholders encountered in working with RWD included the lack of unique device identifiers, capturing key data elements and their appropriate meaning in structured data, limited reliability of diagnosis and procedure codes in structured data, extracting information from unstructured electronic health record (EHR) data, limited capture of long-term study end points, missing data and data sharing. Successful strategies included using manufacturer and supply chain data, leveraging clinical registries and registry reporting processes to collect and aggregate data, querying standardized EHR data, implementing natural language processing algorithms and using multidisciplinary research teams. Conclusion: The Test-Cases identified numerous challenges working with RWD but also opportunities to address these challenges and improve researchers' ability to use RWD to generate evidence on medical devices.

Applying an ELSI lens to real-world data and novel genomic insights for personalized mental healthcare.

Improving the understanding of the complex relationship between genetic predispositions, environmental influences, and sociocultural factors in the development and progression of mental illness is crucial for optimizing treatment efficacy and addressing longstanding health disparities. This paper discusses the ethical, legal, and social implications (ELSI) of recent advancements in biomedical research, particularly in genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and genome-wide environment interaction studies (GWEIS). Despite recent scientific progresses, challenges such as inadequate study methodology (e.g., correlational studies) and lack of diversity within study samples persist. Recent discoveries of several genetic variants of diseases, could augment and improve, or even challenge, existing understanding of the onset and management of mental illness. Leveraging real-world data (RWD), including electronic health record data (EHRs) focused on social determinant of health alongside biobank data, offers further opportunities to enhance the understanding of gene-environment interactions and inform efforts for reducing disparities in mental healthcare. Increased knowledge can support timely, holistic, evidence-based, and personalized care. Addressing ELSI considerations and maximizing the use of RWD is essential for advancing ethical and inclusive psychiatric genetics research, ultimately improving patient outcomes and promoting equitable access to evidence-based treatments.

Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union.

The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to inform decisions about treatment effectiveness and safety. First, we provide an overview of RWD and RWE. Data quality frameworks (DQFs) in the US and EU were examined, including their dimensions and subdimensions. There is some convergence of the conceptual DQFs on specific assessment criteria. Second, we describe a list of screening criteria for assessing the quality of RWD sources. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). In conclusion, this paper provides a perspective on the utilization of RWD and RWE in healthcare decision-making. It covers the types and uses of RWD, data quality frameworks (DQFs), regulatory landscapes, and the potential impact of RWE, as well as the challenges and opportunities for the greater leveraging of RWD to create credible RWE.

Using real-world evidence to complement evidence from randomized controlled trials on oral anticoagulants for stroke prevention.

Mark J. R. Smeets and Suzanne C. Cannegieter discuss the use of real world data to complement data generated by clinical trials of systemic anticoagulants.

Expanding the use of real-world data through the development of user-friendly tools to support drug development: Example of an organ impairment dashboard.

Expanding the use of real-world data through the development of user-friendly tools to support drug development: Example of an organ impairment dashboard.

Proton therapy for head and neck cancer therapy: A real-world data case study from Bulgaria

Head and neck cancers (HNC) are one of the most severe types of cancer in Europe, accounting for almost 4.5% of all cancer types, according to GLOBOCAN. The incidence is geographically variable, with higher rates observed in Eastern and Southern Europe than in Northern and Western Europe. Proton and photon therapies are both available treatment approaches for head and neck cancer, as each has its own unique advantages, characteristics, and considerations. Proton therapy is the newest one and is considered safer among both. The current study aims to analyse the available data for patients with head and neck cancer treated with proton therapy in Bulgaria based on real-world data generated through AI-based patient registries and the current availability of strategic policy documents ensuring the affordability of proton therapy on a national level. We wanted to explore the feasibility of building a national proton therapy centre based on available patients’ information and strategic policy documents. We conducted a 3-year (2020–2022) combined non-interventional retrospective database study on head and neck cancer using secondary use of real-world data from dynamic patient registries and desktop analysis of strategic policy documents for capacity and financial affordability for proton therapy in Bulgaria. The results show that Bulgaria has a strategic policy document that focuses on the need and funding possibilities for establishing this treatment within the country. However, the country lacks the political will to ensure appropriate funding for it. Building a national proton centre is a feasible investment but needs additional detailed budget impact analysis.

Over a decade of experience in total knee arthroplasty with a multiradius design and fixed bearing at a single centre: Data from the Catalan Arthroplasty Registry.

Arthroplasty registers can provide feedback information on the results of arthroplasties performed by a certain institution or surgeon. The use of real-world data to achieve real-world evidence can help evaluate the performance of the implants used and help counselour patients. The main objective of our study was to determine the survivorship of the total knee implant we are currently using. A retrospective cohort study of patients who received a total knee arthroplasty from January 2009 to December 2020 in our hospital was conducted, using data from the Catalan arthroplasty registry and the Catalan health service database. Demographic and surgical data were analysed using the Kaplan-Meier method, log-rank testand Cox proportional hazards models with the R Project software (p < 0.05). A total of 1336 total knee arthroplasties were included in the study, of which992 were women. The causes for revision included aseptic loosening (17), infection (29), instability (13), patellar implantation (13), arthrofibrosis (5) and quadriceps tendon rupture (1).The cumulative risk for revision at 5 years using the Kaplan-Meier method was 6.0% and at 10 years 6.5%. Considering gender, this risk was 7.0% and 7.5% at 5 and 10 years, respectively, in womenand 3.3% in men, both at 5 and 10 years (p = 0.009). A higher risk for revision in women was seen, which is considered statistically significant (p = 0.012). Our survivorship results are comparable to those published in the literature, but with a higher revision risk in women that is only statistically significant for the whole group of reoperations and for patellar implantation, but not for the rest of the diagnoses. Level IV.

Establishing data elements and exchange standards to support long COVID healthcare and research.

The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. Critical data elements for managing long COVID were defined through a consensus-driven approach involving a Technical Expert Panel (TEP). This involved 2 stages: identifying data concepts and establishing electronic exchange standards. The TEP-identified and -approved long COVID data elements were mapped to the HL7 US Core FHIR profiles for syntactic representation, and value sets from standard code systems were developed for semantic representation of the long COVID concepts. Establishing common long COVID data standards through this process, and representing them with the HL7 FHIR standard, facilitates interoperable data collection, benefiting care delivery and patient-centered outcomes research (PCOR) for long COVID. These standards may support initiatives including clinical and pragmatic trials, quality improvement, epidemiologic research, and clinical and social interventions.By building standards-based data collection, this effort accelerates the development of evidence to better understand and deliver effective long COVID interventions and patient and caregiver priorities within the context of MCCs and to advance the delivery of coordinated, person-centered care. The open, collaborative, and consensus-based approach used in this project will enable the sharing of long COVID-related health concerns, interventions, and outcomes for patient-centered care coordination across diverse clinical settings and will facilitate the use of real-world data for long COVID research.

Developmental delay can precede neurologic regression in early onset metachromatic leukodystrophy

ObjectiveMetachromatic leukodystrophy (MLD) is a rare neurodegenerative disorder. Emerging therapies are most effective in the presymptomatic phase, and thus defining this window is critical. We hypothesize that early development delay may precede developmental plateau. With the advent of presymptomatic screening platforms and transformative therapies, it is essential to define the onset of neurologic disease. MethodsThe specific ages of gain and loss of developmental milestones were captured from the medical records of individuals affected by MLD. Milestone acquisition was characterized as: on target (obtained before the age limit of 90th percentile plus 2 standard deviations compared to a normative dataset), delayed (obtained after 90th percentile plus 2 standard deviations), or plateau (skills never gained). Regression was defined as the age at which skills were lost. LI-MLD was defined by age at onset before 2.5 years. ResultsAcross an international cohort, 351 subjects were included (n = 194 LI-MLD subcohort). The median age at presentation of the LI-MLD cohort was 1.4 years (25th–75th %ile: 1.0–1.5). Within the LI-MLD cohort, 75/194 (39%) had developmental delay (or plateau) prior to MLD clinical presentation. Among the LI-MLD cohort with a minimum of 1.5 years of follow-up (n = 187), 73 (39.0%) subjects never attained independent ambulation. Within LI-MLD + delay subcohort, the median time between first missed milestone target to MLD decline was 0.60 years (maximum distance from delay to onset: 1.9 years). InterpretationEarly developmental delay precedes regression in a subset of children affected by LI-MLD, defining the onset of neurologic dysfunction earlier than previously appreciated. The use of realworld data prior to diagnosis revealed an early deviation from typical development. Close monitoring for early developmental delay in presymptomatic individuals may help in earlier diagnosis with important consequences for treatment decisions.

HTA41 Analysis of Intensity of Use of Real-World Data in Single Technology Appraisals of Cancer Drugs By the National Institute for Health and Care Excellence

HTA41 Analysis of Intensity of Use of Real-World Data in Single Technology Appraisals of Cancer Drugs By the National Institute for Health and Care Excellence

Use of Real-World Data and Machine Learning to Screen for Maternal and Paternal Characteristics Associated with Cardiac Malformations.

Effective prevention of cardiac malformations, a leading cause of infant morbidity, is constrained by limited understanding of etiology. The study objective was to screen for associations between maternal and paternal characteristics and cardiac malformations. We selected 720,381 pregnancies linked to live-born infants (n=9,076 cardiac malformations) in 2011-2021 MarketScan US insurance claims data. Odds ratios were estimated with clinical diagnostic and medication codes using logistic regression. Screening of 2,000 associations selected 81 associated codes at the 5% false discovery rate. Grouping of selected codes, using latent semantic analysis and the Apriori-SD algorithm, identified elevated risk with known risk factors, including maternal diabetes and chronic hypertension. Less recognized potential signals included maternal fingolimod or azathioprine use. Signals identified might be explained by confounding, measurement error, and selection bias and warrant further investigation. The screening methods employed identified known risk factors, suggesting potential utility for identifying novel risk factors for other pregnancy outcomes.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

Development of a novel methodology to assess response to lymphoma treatment in real-world data.

e19081 Background: Innovation in the use of real-world data (RWD) for regulatory purposes is an important provision of 21st Century Cures Act. Numerous initiatives to enhance RWD methodologies in oncology drug development are ongoing, including evaluation of treatment response assessment to support effectiveness research. Blinded independent central review (BICR) standardizes response assessment in clinical trials, and corresponding RWD methodologies are needed. This study assess feasibility of BICR using RWD, and evaluates a novel, standardized treatment response methodology among patients with diffuse large B-cell lymphoma (DLBCL). Methods: A retrospective multisite chart review identified adult patients with DLBCL treated with first-line chemoimmunotherapy in US (01JAN2015-31DEC2022). Clinical data, including physician-reported response, were abstracted from medical charts and positron emission tomography-computed tomography (PET-CT) reports at treatment initiation and first response assessment. Lugano 2014 criteria, such as standardized uptake value (SUV) and Deauville score, were extracted and converted to an algorithm (rwLugano). Absence of normal tissue SUV in PET-CT reports was addressed using published values for background, mediastinum, and liver to facilitate a real-world Deauville (rwDeauville) score for rwLugano classification. PET-CTs underwent deidentified digital image transfer for BICR using Lugano 2014 criteria by two lymphoma radiologists. Descriptive analyses assessed BICR feasibility and completeness of rwLugano data elements. Results: Six oncology practices, comprising 77 physicians, abstracted medical records and PET-CT reports for 185 patients. PET-CT scans were available for BICR for 174/185 (94%) patients (41% female, mean age 66 years). BICR discordance for Deauville score at baseline and initial response occurred in 7% (12/174) and 29% (50/174), respectively. For response assessment per Lugano, BICR discordance occurred in 9% (16/174), and were further adjudicated by a third party. Lugano discordance was often related to interpretation of marrow activity and/or new disease sites, highlighting variability in lymphoma treatment response assessment. Medical record review found Deauville score charted for 97 (56%) patients at baseline and 145 (83%) at first response. Tumor SUV was complete (174/174, 100%) at baseline and for all patients (63/63, 100%) not charted as CR at first response. Thus, 29 patients (17%) required calculation of rwDeauville at first response, providing data to calculate rwLugano for all patients in the final cohort (174/174, 100%). Conclusions: This study suggests BICR, though resource intensive, can be performed, and the component measures necessary to assess response per Lugano criteria can be obtained using RWD. Accordingly, rwLugano warrants further analysis for concordance with BICR- and physician-reported response.