Publications by authors named "Craig S Mayer"

6 Publications

  • Page 1 of 1

Identification of Common Data Elements from Pivotal FDA Trials.

AMIA Annu Symp Proc 2020 25;2020:813-822. Epub 2021 Jan 25.

Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH Bethesda, MD.

It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075437PMC
January 2021

The changing patterns of comorbidities associated with human immunodeficiency virus infection, a longitudinal retrospective cohort study of Medicare patients.

Medicine (Baltimore) 2021 Apr;100(16):e25428

The Lister Hill National Center for Biomedical Communications at the National Library of Medicine, National Institutes of Health in the United States, Bethesda, Maryland.

Abstract: The objective of this paper is to determine the temporal trend of the association of 66 comorbidities with human immunodeficiency virus (HIV) infection status among Medicare beneficiaries from 2000 through 2016.We harvested patient level encounter claims from a 17-year long 100% sample of Medicare records. We used the chronic conditions warehouse comorbidity flags to determine HIV infection status and presence of comorbidities. We prepared 1 data set per year for analysis. Our 17 study data sets are retrospective annualized patient level case histories where the comorbidity status reflects if the patient has ever met the comorbidity case definition from the start of the study to the analysis year.We implemented one logistic binary regression model per study year to discover the maximum likelihood estimate (MLE) of a comorbidity belonging to our binary classes of HIV+ or HIV- study populations. We report MLE and odds ratios by comorbidity and year.Of the 66 assessed comorbidities, 35 remained associated with HIV- across all model years, 19 remained associated with HIV+ across all model years. Three comorbidities changed association from HIV+ to HIV- and 9 comorbidities changed association from HIV- to HIV+.The prevalence of comorbidities associated with HIV infection changed over time due to clinical, social, and epidemiological reasons. Comorbidity surveillance can provide important insights into the understanding and management of HIV infection and its consequences.
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http://dx.doi.org/10.1097/MD.0000000000025428DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078399PMC
April 2021

Computerized monitoring of COVID-19 trials, studies and registries in ClinicalTrials.gov registry.

PeerJ 2020 23;8:e10261. Epub 2020 Oct 23.

Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, MD, USA.

Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history "Study Status" is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.
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http://dx.doi.org/10.7717/peerj.10261DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7587053PMC
October 2020

Analysis of data dictionary formats of HIV clinical trials.

PLoS One 2020 5;15(10):e0240047. Epub 2020 Oct 5.

Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, MD, United States of America.

Background: Efforts to define research Common Data Elements try to harmonize data collection across clinical studies.

Objective: Our goal was to analyze the quality and usability of data dictionaries of HIV studies.

Methods: For the clinical domain of HIV, we searched data sharing platforms and acquired a set of 18 HIV related studies from which we analyzed 26 328 data elements. We identified existing standards for creating a data dictionary and reviewed their use. To facilitate aggregation across studies, we defined three types of data dictionary (data element, forms, and permissible values) and created a simple information model for each type.

Results: An average study had 427 data elements (ranging from 46 elements to 9 945 elements). In terms of data type, 48.6% of data elements were string, 47.8% were numeric, 3.0% were date and 0.6% were date-time. No study in our sample explicitly declared a data element as a categorical variable and rather considered them either strings or numeric. Only for 61% of studies were we able to obtain permissible values. The majority of studies used CSV files to share a data dictionary while 22% of the studies used a non-computable, PDF format. All studies grouped their data elements. The average number of groups or forms per study was 24 (ranging between 2 and 124 groups/forms). An accurate and well formatted data dictionary facilitates error-free secondary analysis and can help with data de-identification.

Conclusion: We saw features of data dictionaries that made them difficult to use and understand. This included multiple data dictionary files or non-machine-readable documents, data elements included in data but not in the dictionary or missing data types or descriptions. Building on experience with aggregating data elements across a large set of studies, we created a set of recommendations (called CONSIDER statement) that can guide optimal data sharing of future studies.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240047PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7535029PMC
November 2020

Sharing of Individual Participant Data from Clinical Trials: General Comparison and HIV Use Case.

AMIA Annu Symp Proc 2019 4;2019:647-654. Epub 2020 Mar 4.

Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH Bethesda, MD.

Sharing of individual participant data is encouraged by the International Committee of Medical Journal Editors. We analyzed clinical trial registry data from ClinicalTrials.gov (CTG) and determined the proportion of trials sharing de-identified Individual Participant Data (IPD). We looked at 3,138 medical conditions (as Medical Subject Heading terms). Overall, 10.8% of trials with first registration date after December 1, 2015 answered 'Yes' to plan to share de-identified IPD data. This sharing rate ranges between 0% (biliary tract neoplasms) to 72.2% (meningitis, meningococcal) when analyzed by disease that is focus of a study. Via a predictive model, we found that studies that deposited basic summary results data to CTG results registry, large studies and phase 3 interventional studies are most likely to declare intent to share IPD data. As part of an HIV common data element analysis project, we further compared a body of HIV trials (24% sharing rate) to other diseases.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153161PMC
June 2020

Evaluation of Research Accessibility and Data Elements of HIV Registries.

Curr HIV Res 2019 ;17(4):258-265

Lister Hill National Center for Biomedical Communication, National Library of Medicine, NIH, Bethesda, MD, United States.

Background: Patient registries represent a long-term data collection system that is a platform for performing multiple research studies to generate real-world evidence. Many of these registries use common data elements (CDEs) and link data from Electronic Health Records.

Objective: This study evaluated HIV registry features that contribute to the registry's usability for retrospective analysis of existing registry data or new prospective interventional studies.

Methods: We searched PubMed and ClinicalTrials.gov (CTG) to generate a list of HIV registries. We used the framework developed by the European Medical Agency (EMA) to evaluate the registries by determining the presence of key research features. These features included information about the registry, request and collaboration processes, and available data. We acquired data dictionaries and identified CDEs.

Results: We found 13 HIV registries that met our criteria, 11 through PubMed and 2 through CTG. The prevalence of the evaluated features ranged from all 13 (100%) having published key registry information to 0 having a research contract template. We analyzed 6 data dictionaries and identified 14 CDEs that were present in at least 4 of 6 (66.7%) registry data dictionaries.

Conclusion: The importance of registries as platforms for research data is growing and the presence of certain features, including data dictionaries, contributes to the reuse and secondary research capabilities of a registry. We found some features such as collaboration policies were in the majority of registries while others such as, ethical support, were in a few and are more for future development.
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http://dx.doi.org/10.2174/1570162X17666190924195439DOI Listing
July 2020