Real-world Data Sources Research Articles

Leveraging Electronic Health Records to Meet The Real World Evidence Needs of Hta: A Uk Perspective

The inclusion of real world evidence in HTA submissions has become increasingly desirable. In the UK, databases containing data collected in primary care represent a ready source of real world data however they continue to be underutilised in this field. We sought to review the use of such databases in NICE technology assessments (TAs) and provide recommendations regarding their use in future submissions. The NICE website was searched for keywords relating to the main primary care databases in the UK. All NICE TAs identified through this search were screened and information on the data source and the way the data was used in the submission were extracted. Comments on the data by the evidence review group (ERG) and committee were also extracted and reviewed. A total of 13 NICE TAs were identified. Between 2007 and 2010, data from primary care databases were used in 3 NICE TAs to support arguments regarding the prevalence of asthma and prescribing patterns in asthma and fracture prevention. The ERG/committee did not comment on this data. Between 2011 and 2016, data from primary care databases were used in 10 NICE TAs, with 3 in 2015 and 3 in 2016. In each of these cases the data were used to inform a small number of parameters in an economic model. The data were generally well received by the ERGs/committees. Criticisms of the data typically occurred where the results had been repurposed from a published study. The use of data from UK primary care databases in NICE submissions is increasing. The planning of database studies to support market access should begin early in the drug development process and, through collaboration between the trial team, economic modellers and database experts, can be used to inform a multitude of parameters in the economic model.

PRM13 - Assessing Comparability and Patient Profile of Two Claims Databases in Oncology in Brazil

In Brazil, digitalized real world data (RWD) sources are scarce. Some electronic medical records’ (EMRs) systems are currently being implemented, but most of them are still in an early phase. EMRs and claim data have an important role in generating RWD (especially when compared with classical retrospective studies) due to their ability to compile data on large samples of patients and to speed up study conduction. This study aims to assess and compare the profile of patients from two different cancer claims databases in Brazil (Auditron and FOSP). : Auditron is an auditing claims database of the private healthcare setting and FOSP database includes all cancer diagnoses of 76 institutions in the state of São Paulo. In order to compare both databases, we considered only FOSP patients that underwent chemotherapy and/or hormone therapy, once imported data from Auditron referred exclusively to oncology prescriptions. For both FOSP and Auditron databases, we analyzed data for the year 2015 from the state of Sao Paulo. Descriptive analyses were performed based on the most common types of tumors. We identified 14,742 patients in FOSP database and 1,519 patients with 2,835 prescriptions in Auditron database. Among FOSP patients, 13,554 and 1,189 were from the public and private settings, respectively, while all Auditron patients were from the private setting. The most common cancer types were breast cancer (24.9%, n=3,662), prostate cancer (8.5%, n=1,254), and lung cancer (6.1%, n=892) in FOSP, and breast cancer (29.1%, n=823), prostate cancer (11.6%, n=328), and myeloid leukemia (9.3%, n=263) in Auditron. Although limited to the state of Sao Paulo, FOSP database provides substantial information on cancer treatment, from a large number of patients. Auditron presents a larger geographic coverage on the country and more detailed information on prescriptions, while FOSP does not provide detailed information on prescriptions.

Identification of Real World Data Sources in Cancer in the Nordic Countries

Identification of Real World Data Sources in Cancer in the Nordic Countries

Real-world Data for Clinical Evidence Generation in Oncology.

Conventional cancer clinical trials can be slow and costly, often produce results with limited external validity, and are difficult for patients to participate in. Recent technological advances and a dynamic policy landscape in the United States have created a fertile ground for the use of real-world data (RWD) to improve current methods of clinical evidence generation. Sources of RWD include electronic health records, insurance claims, patient registries, and digital health solutions outside of conventional clinical trials. A definition focused on the original intent of data collected at the point of care can distinguish RWD from conventional clinical trial data. When the intent of data collection at the point of care is research, RWD can be generated using experimental designs similar to those employed in conventional clinical trials, but with several advantages that include gains in efficient execution of studies with an appropriate balance between internal and external validity. RWD can support active pharmacovigilance, insights into the natural history of disease, and the development of external control arms. Prospective collection of RWD can enable evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. PCTs may help address the growing demands for access to experimental therapies while increasing patient participation in cancer clinical trials. Conducting valid real-world studies requires data quality assurance through auditable data abstraction methods and new incentives to drive electronic capture of clinically relevant data at the point of care.

How one creative agency used metadata to unlock value for its clients

In 2016, Anomaly and its newly formed product development team embarked on a digital asset management initiative with the intention of fuelling a larger technology vision that would provide its clients with deeper insights into the work they were creating. To accomplish this, a digital asset management platform was needed to extract as much relevant and meaningful metadata about the company’s creative assets as possible and make that information available as an independent data source. From an advertising industry perspective, this involved a new innovative architecture, custom integrations and an unconventional way of thinking about the practice of digital asset management. This paper details Anomaly’s focus on the metadata, the value brought by said data and how the company was eventually able to provide a deeper level of consumer insights by analysing metadata in combination with other real-world data sources.

Automatic Detection of Verbal Deception

A growing field in computer applications is the use of algorithms to spot the lie. The most promising area within this field is the analysis of the language of the liar since speakers effectively control only the meaning they wish to convey, but not the linguistic style of the communication. With the advent of computational means to analyze language, we now have the ability to recognize differences in the way speakers phrase their lies as opposed to their truths. The main goal of this book is to cover the advances of the last 10 years in automatically discriminating truths from lies. To give the reader a grounding in deception studies, it describes a range of behaviors (physiological, gestural as well as verbal) that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and real-world data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, fraud, and false product reviews. Having established the background, the book concentrates on computational analyses of deceptive verbal behavior, which have enabled the field of deception studies to move from individual cues to overall differences in behavior. The book concludes with a set of open questions that the computational work has generated.

Use of External Controls in Regulatory Decision-Making.

The expanded use of electronic data retrieval systems has greatly expanded the potential sources of real-world data and presents new opportunities for evidence generation outside of the traditional research trial. These data may be used to inform trial design and interpretation as well. Externally controlled trials, such as single-arm and noninferiority designs, have long been used in regulatory decision-making despite the potential flaws based on assumptions of assay sensitivity and constancy.

Entity reconciliation in big data sources: A systematic mapping study

The entity reconciliation (ER) problem aroused much interest as a research topic in today's Big Data era, full of big and open heterogeneous data sources. This problem poses when relevant information on a topic needs to be obtained using methods based on: (i) identifying records that represent the same real world entity, and (ii) identifying those records that are similar but do not correspond to the same real-world entity. ER is an operational intelligence process, whereby organizations can unify different and heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, the complexity that the heterogeneity of data sources involves, the large number of records and differences among languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) of journal articles, conferences and workshops published from 2010 to 2017 to solve the problem described before, first trying to understand the state-of-the-art, and then identifying any gaps in current research. Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process that has resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim to solve ER. The conclusion obtained is that most of the research is based on the operational phase as opposed to the design phase, and most studies have been tested on real-world data sources, where a lot of them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniques based on clustering/blocking and graphs, although the level of automation of the proposals is hardly ever mentioned in the research work.

"Threshold-crossing": A Useful Way to Establish the Counterfactual in Clinical Trials?

A central question in the assessment of benefit/harm of new treatments is: how does the average outcome on the new treatment (the factual) compare to the average outcome had patients received no treatment or a different treatment known to be effective (the counterfactual)? Randomized controlled trials (RCTs) are the standard for comparing the factual with the counterfactual. Recent developments necessitate and enable a new way of determining the counterfactual for some new medicines. For select situations, we propose a new framework for evidence generation, which we call “threshold‐crossing.” This framework leverages the wealth of information that is becoming available from completed RCTs and from real world data sources. Relying on formalized procedures, information gleaned from these data is used to estimate the counterfactual, enabling efficacy assessment of new drugs. We propose future (research) activities to enable “threshold‐crossing” for carefully selected products and indications in which RCTs are not feasible.

Evidence for validity of a national physician and patient-reported, cross-sectional survey in China and UK: the Disease Specific Programme

ObjectiveDiabetes represents a significant challenge for Chinese healthcare providers. Healthcare decision-making is generally based on many data sources, including randomised controlled and real-world studies; however, good-quality data from Chinese diabetes...

Incidence of Gastrointestinal Perforations in Patients with Rheumatoid Arthritis Treated with Tocilizumab from Clinical Trial, Postmarketing, and Real-World Data Sources.

IntroductionThe aim of this study was to use multiple data sources to update information on gastrointestinal perforations (GIPs) during tocilizumab (TCZ) treatment in patients with rheumatoid arthritis (RA).MethodsReporting rates of GIP events were estimated from three distinct patient data sets: a TCZ-IV RA clinical trial all-exposure population, a global TCZ postmarketing safety database population, and a US healthcare claims database population of patients with RA, including patients who received TCZ, anti-tumor necrosis factor (aTNF) agents, or abatacept.ResultsThe clinical trial, global postmarketing, and healthcare claims populations provided 17,906, 382,621, and 3268 patient-years (PYs) of TCZ exposure, respectively. GIP incidence rates [95% confidence interval (CI)] were 1.9 (1.3–2.7), 1.2 (1.1–1.3), and 1.8 (0.7–4.0; specific definition) to 2.8 (1.3–5.2; sensitive definition) per 1000 PYs for the clinical trial, postmarketing, and healthcare claims populations, respectively. The GIP incidence rate (95% CI) for the comparator aTNF healthcare claims population ranged from 0.6 (0.3–1.2) to 0.9 (0.5–1.5) per 1000 PYs, for an absolute rate difference between TCZ and aTNFs of 1.2 (−0.3 to 2.5) to 1.9 (0.0–3.7) per 1000 PYs, corresponding to a number needed to harm between 533 and 828.ConclusionThe TCZ GIP event rates from multiple data sources were consistent with previously reported rates, did not increase over time, and were significantly associated with the number of prior biologics. Comparison of GIP incidence rates among patients with prior biologic exposure suggests that, for every 1000 patients treated with TCZ per year, an additional 1–2 GIP events might occur compared with patients treated with aTNFs.FundingRoche.

Exploring Concordance of Patient-Reported Information on PatientsLikeMe and Medical Claims Data at the Patient Level.

BackgroundWith the emergence of data generated by patient-powered research networks, it is informative to characterize their correspondence with health care system-generated data.ObjectivesThis study explored the linking of 2 disparate sources of real-world data: patient-reported data from a patient-powered research network (PatientsLikeMe) and insurance claims.MethodsActive patients within the PatientsLikeMe community, residing in the United States, aged 18 years or older, with a self-reported diagnosis of multiple sclerosis or Parkinson’s disease (PD) were invited to participate during a 2-week period in December 2014. Patient-reported data were anonymously matched and compared to IMS Health medical and pharmacy claims data with dates of service between December 2009 and December 2014. Patient-level match (identity), diagnosis, and usage of disease-modifying therapies (DMTs) were compared between data sources.ResultsAmong 603 consenting patients, 94% had at least 1 record in the IMS Health dataset; of these, there was 93% agreement rate for multiple sclerosis diagnosis. Concordance on the use of any treatment was 59%, and agreement on reports of specific treatment usage (within an imputed 5-year period) ranged from 73.5% to 100%.ConclusionsIt is possible to match patient identities between the 2 data sources, and the high concordance at multiple levels suggests that the matching process was accurate. Likewise, the high degree of concordance suggests that these patients were able to accurately self-report their diagnosis and, to a lesser degree, their treatment usage. Further studies of linked data types are warranted to evaluate the use of enriched datasets to generate novel insights.

Coupled attribute similarity learning on categorical data.

Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced the co-occurrence of attribute values to explore attribute coupling, but they only present a local picture in analyzing categorical data similarity. This is inadequate for deep analysis, and the computational complexity grows exponentially when the data scale increases. This paper proposes an efficient data-driven similarity learning approach that generates a coupled attribute similarity measure for nominal objects with attribute couplings to capture a global picture of attribute similarity. It involves the frequency-based intra-coupled similarity within an attribute and the inter-coupled similarity upon value co-occurrences between attributes, as well as their integration on the object level. In particular, four measures are designed for the inter-coupled similarity to calculate the similarity between two categorical values by considering their relationships with other attributes in terms of power set, universal set, joint set, and intersection set. The theoretical analysis reveals the equivalent accuracy and superior efficiency of the measure based on the intersection set, particularly for large-scale data sets. Intensive experiments of data structure and clustering algorithms incorporating the coupled dissimilarity metric achieve a significant performance improvement on state-of-the-art measures and algorithms on 13 UCI data sets, which is confirmed by the statistical analysis. The experiment results show that the proposed coupled attribute similarity is generic, and can effectively and efficiently capture the intrinsic and global interactions within and between attributes for especially large-scale categorical data sets. In addition, two new coupled categorical clustering algorithms, i.e., CROCK and CLIMBO are proposed, and they both outperform the original ones in terms of clustering quality on UCI data sets and bibliographic data.

Engineering ontology-based access to real-world data sources

The preparation of existing real-world datasets for publication as high-quality semantic web data is a complex task that requires the concerted execution of a variety of processing steps using a range of different tools. Faced with both changing input data and evolving requirements on the produced output, we face a significant engineering task for schema and data transformation. We argue that to achieve a robust and flexible transformation process, a high-level declarative description is needed, that can be used to drive the entire tool chain. We have implemented this idea for the deployment of ontology-based data access (OBDA) solutions, where semantically annotated views that integrate multiple data sources on different formats are created, based on an ontology and a collection of mappings. Furthermore, we exemplify our approach and show how a single declarative description helps to orchestrate a complete tool chain, beginning with the download of datasets, and through to the installation of the datasets for a variety of tool applications, including data and query transformation processes and reasoning services. Our case study is based on several publicly available tabular and relational datasets concerning the operations of the petroleum industry in Norway. We include a discussion of the relative performance of the used tools on our case study, and an overview of lessons learnt for practical deployment of OBDA on real-world datasets.

Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark

Usual correlations assume linearity. If new generalized correlations satisfy r*(Y |X) > r*(X|Y ), X better predicts Y than vice versa. Then we say that X "causes" Y . Thus, Vinod (2013) revives Granger's instantaneous causality concept. Mooij et al. (2014) and their references seem unaware of causality in econometrics. We propose and illustrate new generalized correlations using benchmark data set Cause Effect Pairs (CEP) that consists of 88 different binary "cause effect pairs" from 31 real world data sources, where the cause is presumed known. Our ability to successfully identify the cause in some 75% of cases means researchers can benefit from using our fairly simple data- analytic R software tools as a first step to save time and expense.

Engineering Ontology-Based Access to Real-World Data Sources

The preparation of existing real-world datasets for publication as high-quality semantic web data is a complex task that requires the concerted execution of a variety of processing steps using a range of different tools. Faced with both changing input data and evolving requirements on the produced output, we face a significant engineering task for schema and data transformation. We argue that to achieve a robust and flexible transformation process, a high-level declarative description is needed, that can be used to drive the entire tool chain. We have implemented this idea for the deployment of ontology-based data access (OBDA) solutions, where semantically annotated views that integrate multiple data sources on different formats are created, based on an ontology and a collection of mappings. Furthermore, we exemplify our approach and show how a single declarative description helps to orchestrate a complete tool chain, beginning with the download of datasets, and through to the installation of the datasets for a variety of tool applications, including data and query transformation processes and reasoning services. Our case study is based on several publicly available tabular and relational datasets concerning the operations of the petroleum industry in Norway. We include a discussion of the relative performance of the used tools on our case study, and an overview of lessons learnt for practical deployment of OBDA on real-world datasets.

Real-World Evidence in Pain Research: A Review of Data Sources

ABSTRACTOutcomes research studies use clinical and administrative data generated in the course of patient care or from patient surveys to examine the effectiveness of treatments. Health care providers need to understand the limitations and strengths of the real-world data sources used in outcomes studies to meaningfully use the results. This paper describes five types of databases commonly used in the United States for outcomes research studies, discusses their strengths and limitations, and provides examples of each within the context of pain treatment. The databases specifically discussed are generated from (1) electronic medical records, which are created from patient-provider interactions; (2) administrative claims, which are generated from providers’ and patients’ transactions with payers; (3) integrated health systems, which are generated by systems that provide both clinical care and insurance benefits and typically represent a combination of electronic medical record and claims data; (4) national surveys, which provide patient-reported responses about their health and behaviors; and (5) patient registries, which are developed to track patients with a given disease or exposure over time for specified purposes, such as population management, safety monitoring, or research.

Anomaly detection in rolling element bearings via hierarchical transition matrices

Rolling element bearings are critical components in nearly all rotating machinery. Even small physical defects can reduce the reliability and lifespan of these devices. Such defects create anomalous patterns in the bearing vibration signature. These patterns can be used to predict component failure and allow preventative maintenance. Previous work has attempted to perform anomaly detection in vibration data from rolling element bearings. This paper presents a novel approach to anomaly detection in bearing signatures using a Hierarchical Transition Matrix Model (HTMM) combined with dimensionality reduction through Symbolic Aggregate Approximation (SAX) and Discrete Wavelet Transforms (DWT). This new method is validated through both simulated and experimental data. Simulated data is generated to demonstrate strong robustness against noise and high dimensionality, and an empirical dataset (into which defects were introduced manually) demonstrates application to real-world data sources. In a ground truth meta-tagged study, our method outperforms other approaches in both speed and accuracy, automatically detecting anomalies in small spans (<0.1s) of data with high reliability (>99%) in real time.

Towards a framework for ontology-based information retrieval services

In this paper, we present a complete ontology-based framework for the extraction and retrieval of semantic information in specific domains. We focus on the recognition of semantic information extracted from data sources and the mapping of this knowledge into ontology. In order to achieve more scalability, we propose an approach for semantic indexing based on entity retrieval model. The proposed approach was validated using an example of ontology in public transportation domain. Finally, we evaluated our system using ontology mapping and real world data sources. Experiment results show that our proposed framework can provide meaningful search results.

Simulation metamodelling with Bayesian networks

This paper introduces the use of Bayesian networks (BNs) as an exploratory metamodelling tool for supporting simulation studies conducted with stochastic simulation models containing multiple inputs and outputs. BN metamodels combine simulation data with available expert knowledge into a non-parametric description of the joint probability distribution of discrete random variables representing simulation inputs and outputs. The distributions of the inputs are determined based on expert knowledge and/or a real-world data source while the conditional distributions of the outputs are estimated from the simulation data. The exploratory use of the BN metamodels is an iterative process including the construction and validation of the BNs and allowing various analyses dealing with the dependencies among the inputs and the outputs, input uncertainty, and inverse reasoning. The results of these analyses are applied to guide and aid the utilization and interpretation of the simulation model under consideration. In addition, the analyses are used for studying the behaviour of the simulated system. The exploratory use is illustrated with an example involving a simulated queue.