Objective: This review aimed to assess the current use and acceptance of real-world data (RWD) and real-world evidence (RWE) in health technology assessment (HTA) process. It additionally aimed to discern stakeholders’ viewpoints concerning RWD and RWE in HTA and illuminate the obstacles, difficulties, prospects, and consequences associated with the incorporation of RWD and RWE into the realm of HTA.Methods: A comprehensive PRISMA-based systematic review was performed in July 2022 in PubMed/Medline, Scopus, IDEAS-RePEc, International HTA database, and Centre for Reviews and Dissemination with ad hoc supplementary search in Google Scholar and international organization websites. The review included pre-determined inclusion criteria while the selection of eligible studies, the data extraction process and quality assessment were carried out using standardized and transparent methods.Results: Twenty-nine (n = 29) studies were included in the review out of 2,115 studies identified by the search strategy. In various global contexts, disparities in RWD utilization were evident, with randomized controlled trials (RCTs) serving as the primary evidence source. RWD and RWE played pivotal roles, surpassing relative effectiveness assessments (REAs) and significantly influencing decision-making and cost-effectiveness analyses. Identified challenges impeding RWD integration into HTA encompassed limited local data access, complexities in non-randomized trial design, data quality, privacy, and fragmentation. Addressing these is imperative for optimal RWD utilization. Incorporating RWD/RWE in HTA yields multifaceted advantages, enhancing understanding of treatment efficacy, resource utilization, and cost analysis, particularly via patient registries. RWE complements assessments of advanced therapy medicinal products (ATMPs) and rare diseases. Local data utilization strengthens HTA, bridging gaps when RCT data is lacking. RWD aids medical device decision-making, cancer drug reassessment, and indirect treatment comparisons. Challenges include data availability, stakeholder acceptance, expertise, and privacy. However, standardization, training, collaboration, and guidance can surmount these barriers, fostering enhanced RWD utilization in HTA.Conclusion: This study highlights the intricate global landscape of RWD and RWE acceptance in HTA. Recognizing regional nuances, addressing methodological challenges, and promoting collaboration are pivotal, among others, for leveraging RWD and RWE effectively in healthcare decision-making.

This is a report of city vehicles and actual MPG compared to EPA estimated MPG. Each line of data is a combination of all the active vehicles on the city’s telematics system broken down into year/make/model/standard type with fueling and usage data. The intent is for each line to represent the sticker MPG and the real-world MPG and how these compare to each other. The report can be found at https://www1.nyc.gov/assets/dcas/downloads/pdf/fleet/NYC-Fleet-Newsletter-306-May-27-2020-Hybrids-Work-Even-Better-in-Reality-Than-in-Theory.pdf.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

Real World Evidence Analytics Market size was valued at USD 2.26 billion in 2019 and is poised to grow from USD 2.45 billion in 2023 to USD 4.97 billion by 2031, growing at a CAGR of 8.2% in the forecast period (2024-2031).

Australian and New Zealand journal of statistics - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

Broad classes of statistical classification algorithms have beendeveloped and applied successfully to a wide range of real worlddomains. In general, ensuring that the particular classificationalgorithm matches the properties of the data is crucial inproviding results that meet the needs of the particular applicationdomain. One way in which the impact of this algorithm/applicationmatch can be alleviated is by using ensembles of classifiers, wherea variety of classifiers (either different types of classifiers ordifferent instantiations of the same classifier) are pooled before afinal classification decision is made. Intuitively, classifierensembles allow the different needs of a difficult problem to behandled by classifiers suited to those particular needs.Mathematically, classifier ensembles provide an extra degree offreedom in the classical bias/variance tradeoff, allowing solutionsthat would be difficult (if not impossible) to reach with only asingle classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we surveyselect applications of ensemble methods to problems that havehistorically been most representative of the difficulties inclassification. In particular, we survey applications of ensemblemethods to remote sensing, person recognition, one vs. allrecognition, and medicine.

Objective: This review aimed to assess the current use and acceptance of real-world data (RWD) and real-world evidence (RWE) in health technology assessment (HTA) process. It additionally aimed to discern stakeholders’ viewpoints concerning RWD and RWE in HTA and illuminate the obstacles, difficulties, prospects, and consequences associated with the incorporation of RWD and RWE into the realm of HTA.Methods: A comprehensive PRISMA-based systematic review was performed in July 2022 in PubMed/Medline, Scopus, IDEAS-RePEc, International HTA database, and Centre for Reviews and Dissemination with ad hoc supplementary search in Google Scholar and international organization websites. The review included pre-determined inclusion criteria while the selection of eligible studies, the data extraction process and quality assessment were carried out using standardized and transparent methods.Results: Twenty-nine (n = 29) studies were included in the review out of 2,115 studies identified by the search strategy. In various global contexts, disparities in RWD utilization were evident, with randomized controlled trials (RCTs) serving as the primary evidence source. RWD and RWE played pivotal roles, surpassing relative effectiveness assessments (REAs) and significantly influencing decision-making and cost-effectiveness analyses. Identified challenges impeding RWD integration into HTA encompassed limited local data access, complexities in non-randomized trial design, data quality, privacy, and fragmentation. Addressing these is imperative for optimal RWD utilization. Incorporating RWD/RWE in HTA yields multifaceted advantages, enhancing understanding of treatment efficacy, resource utilization, and cost analysis, particularly via patient registries. RWE complements assessments of advanced therapy medicinal products (ATMPs) and rare diseases. Local data utilization strengthens HTA, bridging gaps when RCT data is lacking. RWD aids medical device decision-making, cancer drug reassessment, and indirect treatment comparisons. Challenges include data availability, stakeholder acceptance, expertise, and privacy. However, standardization, training, collaboration, and guidance can surmount these barriers, fostering enhanced RWD utilization in HTA.Conclusion: This study highlights the intricate global landscape of RWD and RWE acceptance in HTA. Recognizing regional nuances, addressing methodological challenges, and promoting collaboration are pivotal, among others, for leveraging RWD and RWE effectively in healthcare decision-making.

the simulator provides a good starting point to collect data that describes the studied domain.

Network and loading data for a real-world distribution network in the North-East of England.

This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.

This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset.

The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.

The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).

This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.

This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.

The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).

Global Real World Evidence Solutions market size 2025 was XX Million. Real World Evidence Solutions Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Big Data Analytics for Clinical Research Market Outlook

As per our latest research, the Big Data Analytics for Clinical Research market size reached USD 7.45 billion globally in 2024, reflecting a robust adoption pace driven by the increasing digitization of healthcare and clinical trial processes. The market is forecasted to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 25.54 billion by 2033. This significant growth is primarily attributed to the rising need for real-time data-driven decision-making, the proliferation of electronic health records (EHRs), and the growing emphasis on precision medicine and personalized healthcare solutions. The industry is experiencing rapid technological advancements, making big data analytics a cornerstone in transforming clinical research methodologies and outcomes.

Several key growth factors are propelling the expansion of the Big Data Analytics for Clinical Research market. One of the primary drivers is the exponential increase in clinical data volumes from diverse sources, including EHRs, wearable devices, genomics, and imaging. Healthcare providers and research organizations are leveraging big data analytics to extract actionable insights from these massive datasets, accelerating drug discovery, optimizing clinical trial design, and improving patient outcomes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with big data platforms has further enhanced the ability to identify patterns, predict patient responses, and streamline the entire research process. These technological advancements are reducing the time and cost associated with clinical research, making it more efficient and effective.

Another significant factor fueling market growth is the increasing collaboration between pharmaceutical & biotechnology companies and technology firms. These partnerships are fostering the development of advanced analytics solutions tailored specifically for clinical research applications. The demand for real-world evidence (RWE) and real-time patient monitoring is rising, particularly in the context of post-market surveillance and regulatory compliance. Big data analytics is enabling stakeholders to gain deeper insights into patient populations, treatment efficacy, and adverse event patterns, thereby supporting evidence-based decision-making. Furthermore, the shift towards decentralized and virtual clinical trials is creating new opportunities for leveraging big data to monitor patient engagement, adherence, and safety remotely.

The regulatory landscape is also evolving to accommodate the growing use of big data analytics in clinical research. Regulatory agencies such as the FDA and EMA are increasingly recognizing the value of data-driven approaches for enhancing the reliability and transparency of clinical trials. This has led to the establishment of guidelines and frameworks that encourage the adoption of big data technologies while ensuring data privacy and security. However, the implementation of stringent data protection regulations, such as GDPR and HIPAA, poses challenges related to data integration, interoperability, and compliance. Despite these challenges, the overall outlook for the Big Data Analytics for Clinical Research market remains highly positive, with sustained investments in digital health infrastructure and analytics capabilities.

From a regional perspective, North America currently dominates the Big Data Analytics for Clinical Research market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and strong presence of leading pharmaceutical companies. Europe follows closely, driven by increasing government initiatives to promote health data interoperability and research collaborations. The Asia Pacific region is emerging as a high-growth market, supported by expanding healthcare IT investments, rising clinical trial activities, and growing awareness of data-driven healthcare solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, due to infrastructural and regulatory challenges. Overall, the global market is poised for substantial growth across all major regions over the forecast period.

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.9 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.9 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

United States US: GDP: Real: per Capita data was reported at 53,128.540 USD in 2017. This records an increase from the previous number of 52,319.163 USD for 2016. United States US: GDP: Real: per Capita data is updated yearly, averaging 35,443.919 USD from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 53,128.540 USD in 2017 and a record low of 17,036.885 USD in 1960. United States US: GDP: Real: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Real. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; ;

This paper describes the application of known and novel prognostic algorithms on systems that can be described by low dimensional, potentially nonlinear dynamics. The methods rely on estimating the conditional probability distribution of the output of the system at a future time given knowledge of the current state of the system. We show how to estimate these conditional probabilities using a variety of techniques, including bagged neural networks and kernel methods such as Gaussian Process Regression (GPR). The results are compared with standard method such as the nearest neighbor algorithm. We demonstrate the algorithms on a real-world data set and a simulated data set. The real-world data set consists of the intensity of an NH3 laser. The laser data set has been shown by other authors to exhibit low-dimensional chaos with sudden drops in intensity. The simulated data set is generated from the Lorenz attractor and has known statistical characteristics. On these data sets, we show the evolution of the estimated conditional probability distribution, the way it can act as a prognostic signal, and its use as an early warning system. We also review a novel approach to perform Gaussian Process Regression with large numbers of data points.

Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery

This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the claims.csv file, the AirportCode implies AirportName, as each code should be unique for a given airport.

The file ground_truth.csv is a comma separated file containing approximate functional dependencies. table describes the relation we refer to, lhs and rhs reference two columns of those relations where semantically we found that lhs implies rhs.

The file excluded_candidates.csv and included_candidates.csv list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the g3_prime value was too small.

Dataset References

• adult.csv: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
• claims.csv: TSA Claims Data 2002 to 2006, published by the U.S. Department of Homeland Security.
• dblp10k.csv: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. Made available as DBLP Dataset 2.
• hospital.csv: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.
• t_biocase_... files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.
• tax.csv: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.

United States US: Real Effective Exchange Rate Index data was reported at 117.451 2010=100 in 2017. This records a decrease from the previous number of 117.772 2010=100 for 2016. United States US: Real Effective Exchange Rate Index data is updated yearly, averaging 108.785 2010=100 from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 147.640 2010=100 in 1985 and a record low of 95.102 2010=100 in 2011. United States US: Real Effective Exchange Rate Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Exchange Rates and Real Effective Exchange Rates. Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs.; ; International Monetary Fund, International Financial Statistics.; ;

The statistics , AIC, BIC, A⋆, W⋆, D⋆ and p⋆ for D1, D2 and D3.

Graph and download economic data for Constant GDP per capita for the World (NYGDPPCAPKDWLD) from 1960 to 2024 about World, per capita, real, and GDP.