Real-World Data

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Erbil Unsal - One of the best experts on this subject based on the ideXlab platform.

Jonathan Hirsch - One of the best experts on this subject based on the ideXlab platform.

Eleanor Mattern - One of the best experts on this subject based on the ideXlab platform.

  • education for real world Data science roles part 2 a translational approach to curriculum development
    International Journal of Digital Curation, 2017
    Co-Authors: Liz Lyon, Eleanor Mattern
    Abstract:

    This study reports on the findings from Part 2 of a small-scale analysis of requirements for Real-World Data science positions and examines three further Data science roles: Data analyst, Data engineer and Data journalist. The study examines recent job descriptions and maps their requirements to the current curriculum within the graduate MLIS and Information Science and Technology Masters Programs in the School of Information Sciences (iSchool) at the University of Pittsburgh. From this mapping exercise, model ‘course pathways’ and module ‘stepping stones’ have been identified, as well as course topic gaps and opportunities for collaboration with other Schools. Competency in four specific tools or technologies was required by all three roles (Microsoft Excel, R, Python and SQL), as well as collaborative skills (with both teams of colleagues and with clients). The ability to connect the educational curriculum with Real-World positions is viewed as further validation of the translational approach being developed as a foundational principle of the current MLIS curriculum review process

Sebastian Schneeweiss - One of the best experts on this subject based on the ideXlab platform.

  • using real world Data to predict findings of an ongoing phase iv cardiovascular outcome trial cardiovascular safety of linagliptin versus glimepiride
    Diabetes Care, 2019
    Co-Authors: Elisabetta Patorno, Sebastian Schneeweiss, Chandrasekar Gopalakrishnan, David Martin, Jessica M Franklin
    Abstract:

    OBJECTIVE Using Real-World Data (RWD) from three U.S. claims Data sets, we aim to predict the findings of the CARdiovascular Outcome Trial of LINAgliptin Versus Glimepiride in Type 2 Diabetes (CAROLINA) trial comparing linagliptin versus glimepiride in patients with type 2 diabetes (T2D) at increased cardiovascular risk by using a novel framework that requires passing prespecified validity checks before analyzing the primary outcome. RESEARCH DESIGN AND METHODS Within Medicare and two commercial claims Data sets (May 2011–September 2015), we identified a 1:1 propensity score–matched (PSM) cohort of T2D patients 40–85 years old at increased cardiovascular risk who initiated linagliptin or glimepiride by adapting eligibility criteria from CAROLINA. PSM was used to balance >120 confounders. Validity checks included the evaluation of expected power, covariate balance, and two control outcomes for which we expected a positive association and a null finding. We registered the protocol (NCT03648424) before evaluating the composite cardiovascular outcome based on CAROLINA’s primary end point. Hazard ratios (HR) and 95% CIs were estimated in each Data source and pooled with a fixed-effects meta-analysis. RESULTS We identified 24,131 PSM pairs of linagliptin and glimepiride initiators with sufficient power for noninferiority (>98%). Exposure groups achieved excellent covariate balance, including key laboratory results, and expected associations between glimepiride and hypoglycemia (HR 2.38 [95% CI 1.79–3.13]) and between linagliptin and end-stage renal disease (HR 1.08 [0.66–1.79]) were replicated. Linagliptin was associated with a 9% decreased risk in the composite cardiovascular outcome with a CI including the null (HR 0.91 [0.79–1.05]), in line with noninferiority. CONCLUSIONS In a nonrandomized RWD study, we found that linagliptin has noninferior risk of a composite cardiovascular outcome compared with glimepiride.

  • when and how can real world Data analyses substitute for randomized controlled trials
    Clinical Pharmacology & Therapeutics, 2017
    Co-Authors: Jessica M Franklin, Sebastian Schneeweiss
    Abstract:

    Regulators consider randomized controlled trials (RCTs) as the gold standard for evaluating the safety and effectiveness of medications, but their costs, duration, and limited generalizability have caused some to look for alternatives. Real world evidence based on Data collected outside of RCTs, such as registries and longitudinal healthcare Databases, can sometimes substitute for RCTs, but concerns about validity have limited their impact. Greater reliance on such real world Data (RWD) in regulatory decision making requires understanding why some studies fail while others succeed in producing results similar to RCTs. Key questions when considering whether RWD analyses can substitute for RCTs for regulatory decision making are WHEN one can study drug effects without randomization and HOW to implement a valid RWD analysis if one has decided to pursue that option. The WHEN is primarily driven by externalities not controlled by investigators, whereas the HOW is focused on avoiding known mistakes in RWD analyses.

Giovanni Fattore - One of the best experts on this subject based on the ideXlab platform.

  • a scoping review of core outcome sets and their mapping onto real world Data using prostate cancer as a case study
    BMC Medical Research Methodology, 2020
    Co-Authors: Michela Meregaglia, Oriana Ciani, Helen Banks, Maximilian Salcherkonrad, Caroline Carney, Sahan Jayawardana, Paula R Williamson, Giovanni Fattore
    Abstract:

    Background: A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected Real-World Data (RWD) sources. Methods: We conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative Database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A 'mapping' exercise was conducted between the COS identified and RWD routinely collected in selected European countries. Results: Eleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as 'COS for clinical trials and clinical research', 2 as 'COS for practice' and 1 as 'COS patient reported outcomes'. Recommended outcomes were mainly categorized into 'mortality and survival' (17%), 'outcomes related to neoplasm' (18%), and 'renal and urinary outcomes' (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD 'scope specification' domain but not the 'stakeholders involved' and 'consensus process' domains. About 72% overlap existed between COS and linked administrative Data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups. Conclusions: This scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative Data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different Data sources and the interoperability of systems to follow larger patient groups in RWD is required.