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Osborne V, Goodin A, Brown J, Winterstein AG, Bate A, Cohet C, Pont L, Moeny D, Klungel O, Pinheiro S, Seeger J, Chan KA, Edlavitch S, Tilson H, Layton D. Updated core competencies in pharmacoepidemiology to inform contemporary curricula and training for academia, government, and industry. Pharmacoepidemiol Drug Saf 2024; 33:e5789. [PMID: 38629216 DOI: 10.1002/pds.5789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 03/07/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE The first paper to specify the core content of pharmacoepidemiology as a profession was published by an ISPE (International Society for Pharmacoepidemiology) workgroup in 2012 (Jones JK et al. PDS 2012; 21[7]:677-689). Due to the broader and evolving scope of pharmacoepidemiology, ISPE considers it important to proactively identify, update and expand the list of core competencies to inform curricula of education programs; thus, better positioning pharmacoepidemiologists across academic, government (including regulatory), and industry positions. The aim of this project was to update the list of core competencies in pharmacoepidemiology. METHODS To ensure applicability of findings to multiple areas, a working group was established consisting of ISPE members with positions in academia, industry, government, and other settings. All competencies outlined by Jones et al. were extracted from the initial manuscript and presented to the working group for review. Expert-based judgments were collated and used to identify consensus. It was noted that some competencies could contribute to multiple groups and could be directly or indirectly related to a group. RESULTS Five core domains were proposed: (1) Epidemiology, (2) Clinical Pharmacology, (3) Regulatory Science, (4) Statistics and data science, and (5) Communication and other professional skills. In total, 55 individual competencies were proposed, of which 25 were new competencies. No competencies from the original work were dropped but aggregation or amendments were made where considered necessary. CONCLUSIONS While many core competencies in pharmacoepidemiology have remained the same over the past 10 years, there have also been several updates to reflect new and emerging concepts in the field.
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Affiliation(s)
| | | | | | | | | | | | - Lisa Pont
- University of Technology Sydney, Sydney, Australia
| | - David Moeny
- Food & Drug Administration, Silver Spring, USA
| | | | | | | | | | | | - Hugh Tilson
- University of North Carolina, Chapel Hill, USA
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2
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Dreyer NA, Mack CD. Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice. Pragmat Obs Res 2023; 14:101-110. [PMID: 37786592 PMCID: PMC10541678 DOI: 10.2147/por.s396024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 09/19/2023] [Indexed: 10/04/2023] Open
Abstract
Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.
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Zhao R, Zhang W, Zhang Z, He C, Xu R, Tang X, Wang B. Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review. BMC Med Res Methodol 2023; 23:152. [PMID: 37386371 PMCID: PMC10308622 DOI: 10.1186/s12874-023-01960-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 05/31/2023] [Indexed: 07/01/2023] Open
Abstract
OBJECTIVE Real-world data (RWD) and real-world evidence (RWE) have been paid more and more attention in recent years. We aimed to evaluate the reporting quality of cohort studies using real-world data (RWD) published between 2013 and 2021 and analyze the possible factors. METHODS We conducted a comprehensive search in Medline and Embase through the OVID interface for cohort studies published from 2013 to 2021 on April 29, 2022. Studies aimed at comparing the effectiveness or safety of exposure factors in the real-world setting were included. The evaluation was based on the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Agreement for inclusion and evaluation was calculated using Cohen's kappa. Pearson chi-square test or Fisher's exact test and Mann-Whitney U test were used to analyze the possible factors, including the release of RECORD, journal IFs, and article citations. Bonferroni's correction was conducted for multiple comparisons. Interrupted time series analysis was performed to display the changes in report quality over time. RESULTS 187 articles were finally included. The mean ± SD of the percentage of adequately reported items in the 187 articles was 44.7 ± 14.3 with a range of 11.1-87%. Of 23 items, the adequate reporting rate of 10 items reached 50%, and the reporting rate of some vital items was inadequate. After Bonferroni's correction, the reporting of only one item significantly improved after the release of RECORD and there was no significant improvement in the overall report quality. For interrupted time series analysis, there were no significant changes in the slope (p = 0.42) and level (p = 0.12) of adequate reporting rate. The journal IFs and citations were respectively related to 2 areas and the former significantly higher in high-reporting quality articles. CONCLUSION The endorsement of the RECORD cheklist was generally inadequate in cohort studies using RWD and has not improved in recent years. We encourage researchers to endorse relevant guidelines when utilizing RWD for research.
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Affiliation(s)
- Ran Zhao
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wen Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - ZeDan Zhang
- Traditional Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chang He
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Rong Xu
- Guang'anmeng Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - XuDong Tang
- China Academy of Chinese Medical Sciences, Beijing, China.
| | - Bin Wang
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
- Traditional Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing, China.
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Wang SV, Pottegård A, Crown W, Arlett P, Ashcroft DM, Benchimol EI, Berger ML, Crane G, Goettsch W, Hua W, Kabadi S, Kern DM, Kurz X, Langan S, Nonaka T, Orsini L, Perez-Gutthann S, Pinheiro S, Pratt N, Schneeweiss S, Toussi M, Williams RJ. HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force. Pharmacoepidemiol Drug Saf 2023; 32:44-55. [PMID: 36215113 PMCID: PMC9771861 DOI: 10.1002/pds.5507] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/17/2022] [Accepted: 06/28/2022] [Indexed: 02/06/2023]
Abstract
PROBLEM Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. WHAT WE DID The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The overarching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.
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Affiliation(s)
| | | | | | | | | | - Eric I Benchimol
- 1. Department of Paediatrics and Institute of Health Policy, Management and Evaluation, The Hospital for Sick Children, University of Toronto, Toronto, Canada,2. Child Health Evaluative Sciences, SickKids Research Institute, Toronto, Canada,3. ICES, Toronto, Canada
| | | | | | - Wim Goettsch
- The National Health Care Institute, Diemen, and Utrecht University, Utrecht, the Netherlands
| | - Wei Hua
- US Food and Drug Administration
| | | | | | | | | | | | | | | | | | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia
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Data linkage of two national databases: Lessons learned from linking the Dutch Arthroplasty Register with the Dutch Foundation for Pharmaceutical Statistics. PLoS One 2023; 18:e0282519. [PMID: 36888631 PMCID: PMC9994672 DOI: 10.1371/journal.pone.0282519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 02/16/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND To provide guidance on data linkage in case of non-unique identifiers, we present a case study linking the Dutch Foundation for Pharmaceutical Statistics and Dutch Arthroplasty Register to investigate opioid prescriptions before/after arthroplasty. METHODS Deterministic data linkage was used. Records were linked on: sex, birthyear, postcode, surgery date, or thromboprophylaxis initiation as a proxy for the surgery date. Different postcodes were used, depending on availability: patient postcode (available from 2013 onwards), hospital postcode with codes for physicians/hospitals, and hospital postcode with catchment area. Linkage was assessed in several groups: linked arthroplasties, linked on patient postcode, linked on patient postcode, and low-molecular-weight heparin(LWMH). Linkage quality was assessed by checking prescriptions after death, antibiotics after revision for infection, and presence of multiple prostheses. Representativeness was assessed by comparing the patient-postcode-LMWH group with the remaining arthroplasties. External validation was performed by comparing our opioid prescription rates with those derived from datasets from Statistics Netherlands. RESULTS We linked 317,899 arthroplasties on patient postcode/hospital postcode(48%). Linkage on the hospital postcode appeared insufficient. Linkage uncertainty ranged from roughly 30% in all arthroplasties to 10-21% in the patient-postcode-LMWH-group. This subset resulted in 166.357(42%) linked arthroplasties after 2013 with somewhat younger age, fewer females, and more often osteoarthritis than other indications compared to the other arthroplasties. External validation showed similar increases in opioid prescription rates. CONCLUSIONS After identifier selection, checking data availability and internal validity, assessing representativeness, and externally validating our results we found sufficient linkage quality in the patient-postcode-LMWH-group, which consisted of around 42% of the arthroplasties performed after 2013.
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Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis. J Clin Epidemiol 2023; 153:91-101. [PMID: 36400263 PMCID: PMC9671552 DOI: 10.1016/j.jclinepi.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/27/2022] [Accepted: 11/09/2022] [Indexed: 11/19/2022]
Abstract
The global COVID-19 pandemic has generated enormous morbidity and mortality, as well as large health system disruptions including changes in use of prescription medications, outpatient encounters, emergency department admissions, and hospitalizations. These pandemic-related disruptions are reflected in real-world data derived from electronic medical records, administrative claims, disease or medication registries, and mobile devices. We discuss how pandemic-related disruptions in healthcare utilization may impact the conduct of noninterventional studies designed to characterize the utilization and estimate the effects of medical interventions on health-related outcomes. Using hypothetical studies, we highlight consequences that the pandemic may have on study design elements including participant selection and ascertainment of exposures, outcomes, and covariates. We discuss the implications of these pandemic-related disruptions on possible threats to external validity (participant selection) and internal validity (for example, confounding, selection bias, missing data bias). These concerns may be amplified in populations disproportionately impacted by COVID-19, such as racial/ethnic minorities, rural residents, or people experiencing poverty. We propose a general framework for researchers to carefully consider during the design and analysis of noninterventional studies that use real-world data from the COVID-19 era.
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Marsolo K, Kiernan D, Toh S, Phua J, Louzao D, Haynes K, Weiner M, Angulo F, Bailey C, Bian J, Fort D, Grannis S, Krishnamurthy AK, Nair V, Rivera P, Silverstein J, Zirkle M, Carton T. Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network. J Am Med Inform Assoc 2022; 30:447-455. [PMID: 36451264 PMCID: PMC9933062 DOI: 10.1093/jamia/ocac229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/16/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network. MATERIAL AND METHODS Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population. RESULTS There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%. DISCUSSION The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population. CONCLUSIONS This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.
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Affiliation(s)
- Keith Marsolo
- Corresponding Author: Keith Marsolo, PhD, Department of Population Health Sciences, Duke University School of Medicine, 215 Morris Street, Durham, NC 27710, USA;
| | - Daniel Kiernan
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | | | - Darcy Louzao
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kevin Haynes
- Scientific Affairs, HealthCore, Inc., Wilmington, Delaware, USA
| | - Mark Weiner
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Francisco Angulo
- Department of Medicine, Cook County Health and Hospital System, Chicago, Illinois, USA
| | - Charles Bailey
- Department of Pediatrics, Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jiang Bian
- Department of Health Outcomes and Bioinformatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Daniel Fort
- Center for Outcomes and Health Services Research, Ochsner Health, New Orleans, Louisiana, USA
| | - Shaun Grannis
- Regenstrief Institute, Indiana University, Indianapolis, Indiana, USA
| | | | | | | | - Jonathan Silverstein
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Thomas Carton
- Louisiana Public Health Institute, New Orleans, Louisiana, USA
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8
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Wang SV, Pottegård A, Crown W, Arlett P, Ashcroft DM, Benchimol EI, Berger ML, Crane G, Goettsch W, Hua W, Kabadi S, Kern DM, Kurz X, Langan S, Nonaka T, Orsini L, Perez-Gutthann S, Pinheiro S, Pratt N, Schneeweiss S, Toussi M, Williams RJ. HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:1663-1672. [PMID: 36241338 DOI: 10.1016/j.jval.2022.09.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/28/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. METHODS The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.
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Affiliation(s)
- Shirley V Wang
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | | | | | | | | | - Eric I Benchimol
- Child Health Evaluative Sciences, SickKids Research Institute, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada; Department of Paediatrics and Institute of Health Policy, Management and Evaluation, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Wim Goettsch
- The National Health Care Institute, Diemen, The Netherlands; Utrecht University, Utrecht, The Netherlands
| | - Wei Hua
- US Food and Drug Administration, Silver Springs, Maryland, USA
| | - Shaum Kabadi
- Sanofi-Aventis US LLC, North Potomac, Maryland, USA
| | - David M Kern
- Janssen Research & Development, LLC, Philadelphia, Pennsylvania, USA
| | | | | | | | | | | | - Simone Pinheiro
- US Food and Drug Administration, Silver Springs, Maryland, USA
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, South Australia, Australia
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Levenson M, He W, Chen L, Dharmarajan S, Izem R, Meng Z, Pang H, Rockhold F. Statistical consideration for fit-for-use real-world data to support regulatory decision making in drug development. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2120533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
| | - Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Li Chen
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | | | - Rima Izem
- Novartis Institutes for BioMedical Research Basel, Basel, Basel-Stadt, CH
| | | | | | - Frank Rockhold
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
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Penberthy LT, Rivera DR, Lund JL, Bruno MA, Meyer AM. An overview of real-world data sources for oncology and considerations for research. CA Cancer J Clin 2022; 72:287-300. [PMID: 34964981 DOI: 10.3322/caac.21714] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/12/2021] [Accepted: 11/18/2021] [Indexed: 12/11/2022] Open
Abstract
Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.
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Affiliation(s)
- Lynne T Penberthy
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Donna R Rivera
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Jennifer L Lund
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Melissa A Bruno
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Anne-Marie Meyer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Nechuta S, Mukhopadhyay S, Golladay M, Rainey J, Krishnaswami S. Trends, patterns, and maternal characteristics of opioid prescribing during pregnancy in a large population-based cohort study. Drug Alcohol Depend 2022; 233:109331. [PMID: 35149439 PMCID: PMC10838571 DOI: 10.1016/j.drugalcdep.2022.109331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Opioid use during pregnancy has been associated with adverse maternal and infant health outcomes. Prescription drug monitoring programs (PDMP) provide a population-based source of prescription data. We linked statewide PDMP and birth certificate data in Tennessee (TN) to determine patterns of prescription opioid and benzodiazepine use during pregnancy. METHODS We constructed a cohort of 311,217 live singleton births from 2013 to 2016 with prescription history from 90 days before pregnancy to birth. Descriptive statistics were used to describe opioid prescription patterns during pregnancy overall, by maternal characteristics and by year. Multivariable logistic regression models estimated adjusted odds ratios and 95% confidence intervals for factors associated with prescription use. RESULTS The prevalence of prescription use during pregnancy was 14.1% for opioid analgesics, 1.6% buprenorphine for medication-assisted treatment, and 2.6% for benzodiazepines. The prevalence of opioid analgesic use decreased from 16.6% (2013) to 11.8% (2016) (ptrend< 0.001). About 25% used for > 7 and 9.7% for > 30 days' supply. The most common types were hydrocodone (9.3%), codeine (3.4%), and oxycodone (2.9%). In adjusted models, lower education, lower income, pre-pregnancy obesity and smoking during pregnancy were associated with increased odds of any opioid and opioid analgesic use. CONCLUSION(S) Despite the encouraging trend of decreasing use of prescription opioid analgesics, the overall prevalence remained close to 12% with many women using for long durations. Use was associated with lower socioeconomic status, obesity, and prenatal smoking. Findings highlight the need for maternal education and resources, and provider support for implementation of evidence-based care.
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Affiliation(s)
- Sarah Nechuta
- Grand Valley State University, Department of Public Health, College of Health Professions, Grand Rapids, MI 49503, USA; Tennessee Department of Health, Office of Informatics and Analytics, Nashville, TN 37243, USA.
| | - Sutapa Mukhopadhyay
- Tennessee Department of Health, Office of Informatics and Analytics, Nashville, TN 37243, USA
| | - Molly Golladay
- Tennessee Department of Health, Office of Informatics and Analytics, Nashville, TN 37243, USA; Tennessee Department of Health, Office of the State Chief Medical Examiner, Nashville, TN 37243, USA
| | - Jacob Rainey
- Tennessee Department of Health, Office of Informatics and Analytics, Nashville, TN 37243, USA; Johns Hopkins University, Department of Mental Health, 624 N. Broadway, Baltimore, MD 21205, USA
| | - Shanthi Krishnaswami
- Tennessee Department of Health, Office of Informatics and Analytics, Nashville, TN 37243, USA
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12
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Sun JW, Wang R, Li D, Toh S. Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias. Am J Epidemiol 2022; 191:711-723. [PMID: 35015823 DOI: 10.1093/aje/kwab299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/12/2022] Open
Abstract
Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort's (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.
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13
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Liang L, Hu J, Sun G, Hong N, Wu G, He Y, Li Y, Hao T, Liu L, Gong M. Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources. Drug Saf 2022; 45:511-519. [PMID: 35579814 PMCID: PMC9112260 DOI: 10.1007/s40264-022-01170-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2022] [Indexed: 01/28/2023]
Abstract
With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings.
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Affiliation(s)
- Likeng Liang
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Jifa Hu
- The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Sun
- Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region, The Affiliated Cancer Hospital of Xinjiang Medical University, Ürümqi, China
| | - Na Hong
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Ge Wu
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Yuejun He
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Yong Li
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Tianyong Hao
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Li Liu
- Institute of Health Management, Southern Medical University, Guangzhou, China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, Guangzhou, China
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14
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Taylor JA, Crowe S, Espuny Pujol F, Franklin RC, Feltbower RG, Norman LJ, Doidge J, Gould DW, Pagel C. The road to hell is paved with good intentions: the experience of applying for national data for linkage and suggestions for improvement. BMJ Open 2021; 11:e047575. [PMID: 34413101 PMCID: PMC8378388 DOI: 10.1136/bmjopen-2020-047575] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND We can improve healthcare services by better understanding current provision. One way to understand this is by linking data sets from clinical and national audits, national registries and other National Health Service (NHS) encounter data. However, getting to the point of having linked national data sets is challenging. OBJECTIVE We describe our experience of the data application and linkage process for our study 'LAUNCHES QI', and the time, processes and resource requirements involved. To help others planning similar projects, we highlight challenges encountered and advice for applications in the current system as well as suggestions for system improvements. FINDINGS The study set up for LAUNCHES QI began in March 2018, and the process through to data acquisition took 2.5 years. Several challenges were encountered, including the amount of information required (often duplicate information in different formats across applications), lack of clarity on processes, resource constraints that limit an audit's capacity to fulfil requests and the unexpected amount of time required from the study team. It is incredibly difficult to estimate the resources needed ahead of time, and yet necessary to do so as early on as funding applications. Early decisions can have a significant impact during latter stages and be hard to change, yet it is difficult to get specific information at the beginning of the process. CONCLUSIONS The current system is incredibly complex, arduous and slow, stifling innovation and delaying scientific progress. NHS data can inform and improve health services and we believe there is an ethical responsibility to use it to do so. Streamlining the number of applications required for accessing data for health services research and providing clarity to data controllers could facilitate the maintenance of stringent governance, while accelerating scientific studies and progress, leading to swifter application of findings and improvements in healthcare.
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Affiliation(s)
- Julie A Taylor
- Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK
| | - Sonya Crowe
- Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK
| | - Ferran Espuny Pujol
- Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK
| | - Rodney C Franklin
- Paediatric Cardiology Department, Royal Brompton and Harefield NHS Foundation Trust, London, UK
| | | | - Lee J Norman
- Paediatric Intensive Care Audit Network, University of Leeds, Leeds, UK
| | - James Doidge
- Intensive Care National Audit and Research Centre, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Christina Pagel
- Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK
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15
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Gao L, Leung MTY, Li X, Chui CSL, Wong RSM, Au Yeung SL, Chan EWW, Chan AYL, Chan EW, Wong WHS, Lee TMC, Rao N, Wing YK, Lum TYS, Leung GM, Ip P, Wong ICK. Linking cohort-based data with electronic health records: a proof-of-concept methodological study in Hong Kong. BMJ Open 2021; 11:e045868. [PMID: 34158297 PMCID: PMC8220454 DOI: 10.1136/bmjopen-2020-045868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES Data linkage of cohort-based data and electronic health records (EHRs) has been practised in many countries, but in Hong Kong there is still a lack of such research. To expand the use of multisource data, we aimed to identify a feasible way of linking two cohorts with EHRs in Hong Kong. METHODS Participants in the 'Children of 1997' birth cohort and the Chinese Early Development Instrument (CEDI) cohort were separated into several batches. The Hong Kong Identity Card Numbers (HKIDs) of each batch were then uploaded to the Hong Kong Clinical Data Analysis and Reporting System (CDARS) to retrieve EHRs. Within the same batch, each participant has a unique combination of date of birth and sex which can then be used for exact matching, as no HKID will be returned from CDARS. Raw data collected for the two cohorts were checked for the mismatched cases. After the matching, we conducted a simple descriptive analysis of attention deficit hyperactivity disorder (ADHD) information collected in the CEDI cohort via the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) and EHRs. RESULTS In total, 3473 and 910 HKIDs in the birth cohort and CEDI cohort were separated into 44 and 5 batches, respectively, and then submitted to the CDARS, with 100% and 97% being valid HKIDs respectively. The match rates were confirmed to be 100% and 99.75% after checking the cohort data. From our illustration using the ADHD information in the CEDI cohort, 36 (4.47%) individuals had ADHD-Combined score over the clinical cut-off in the SWAN survey, and 68 (8.31%) individuals had ADHD records in EHRs. CONCLUSIONS Using date of birth and sex as identifiable variables, we were able to link the cohort data and EHRs with high match rates. This method will assist in the generation of databases for future multidisciplinary research using both cohort data and EHRs.
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Affiliation(s)
- Le Gao
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Miriam T Y Leung
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
| | - Xue Li
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Celine S L Chui
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Rosa S M Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Department of Social Work and Social Administration, Faculty of Social Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Edward W W Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
| | - Adrienne Y L Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
- Groningen Research Institute of Pharmacy, Unit of PharmacoTherapy, -Epidemiology and -Economics, University of Groningen, Groningen, The Netherlands
| | - Esther W Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
| | - Wilfred H S Wong
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Tatia M C Lee
- Department of Psychology, The University of Hong Kong, Hong Kong, Hong Kong
| | - Nirmala Rao
- Faculty of Education, The University of Hong Kong, Hong Kong, Hong Kong
| | - Yun Kwok Wing
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Terry Y S Lum
- Department of Social Work and Social Administration, Faculty of Social Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Gabriel M Leung
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Patrick Ip
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Ian C K Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong, Hong Kong
- Research Department of Practice and Policy, UCL School of Pharmacy, University College London, London, UK
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16
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Bourke A, Dixon WG, Roddam A, Lin KJ, Hall GC, Curtis JR, van der Veer SN, Soriano-Gabarró M, Mills JK, Major JM, Verstraeten T, Francis MJ, Bartels DB. Incorporating patient generated health data into pharmacoepidemiological research. Pharmacoepidemiol Drug Saf 2020; 29:1540-1549. [PMID: 33146896 DOI: 10.1002/pds.5169] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 09/17/2020] [Accepted: 10/31/2020] [Indexed: 01/18/2023]
Abstract
Epidemiology and pharmacoepidemiology frequently employ Real-World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient-centric world, PGHD incorporated into more traditional Real-Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results.
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Affiliation(s)
| | - William G Dixon
- Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, UK
| | | | - Kueiyu Joshua Lin
- Brigham and Women's & Department of Medicine, Boston, Massachusetts, USA
| | | | - Jeffrey R Curtis
- Division of Clinical Immunology & Rheumatology, The University of Birmingham, Birmingham, Alabama, USA
| | - Sabine N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | | | | | - Jacqueline M Major
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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17
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Cocoros NM, Arlett P, Dreyer NA, Ishiguro C, Iyasu S, Sturkenboom M, Zhou W, Toh S. The Certainty Framework for Assessing Real-World Data in Studies of Medical Product Safety and Effectiveness. Clin Pharmacol Ther 2020; 109:1189-1196. [PMID: 32911562 DOI: 10.1002/cpt.2045] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 08/22/2020] [Indexed: 12/21/2022]
Abstract
A fundamental question in using real-world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort-defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit-for-purposefulness of real-world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.
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Affiliation(s)
- Noelle M Cocoros
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Peter Arlett
- Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.,London School of Hygiene and Tropic Medicine, London, UK
| | - Nancy A Dreyer
- Center for Advanced Evidence Generation, IQVIA Real World Solutions, Cambridge, Massachusetts, USA
| | - Chieko Ishiguro
- Division of Epidemiology, Office of Medical Informatics and Epidemiology, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Solomon Iyasu
- Center for Observational and Real-world Evidence, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Miriam Sturkenboom
- Julius Center, Global Health, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wei Zhou
- Center for Observational and Real-world Evidence, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
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18
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Gokhale M, Stürmer T, Buse JB. Real-world evidence: the devil is in the detail. Diabetologia 2020; 63:1694-1705. [PMID: 32666226 PMCID: PMC7448554 DOI: 10.1007/s00125-020-05217-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/01/2020] [Indexed: 12/12/2022]
Abstract
Much has been written about real-world evidence (RWE), a concept that offers an understanding of the effects of healthcare interventions using routine clinical data. The reflection of diverse real-world practices is a double-edged sword that makes RWE attractive but also opens doors to several biases that need to be minimised both in the design and analytical phases of non-experimental studies. Additionally, it is critical to ensure that researchers who conduct these studies possess adequate methodological expertise and ability to accurately implement these methods. Critical design elements to be considered should include a clearly defined research question using a causal inference framework, choice of a fit-for-purpose data source, inclusion of new users of a treatment with comparators that are as similar as possible to that group, accurately classifying person-time and deciding censoring approaches. Having taken measures to minimise bias 'by design', the next step is to implement appropriate analytical techniques (for example propensity scores) to minimise the remnant potential biases. A clear protocol should be provided at the beginning of the study and a report of the results after, including caveats to consider. We also point the readers to readings on some novel analytical methods as well as newer areas of application of RWE. While there is no one-size-fits-all solution to evaluating RWE studies, we have focused our discussion on key methods and issues commonly encountered in comparative observational cohort studies with the hope that readers are better equipped to evaluate non-experimental studies that they encounter in the future. Graphical abstract.
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Affiliation(s)
- Mugdha Gokhale
- Pharmacoepidemiology, Center for Observational & Real-World Evidence, Merck, 770 Sumneytown Pike, West Point, PA, 19486, USA.
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - John B Buse
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
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