Development of a unified clinical trial database for Alzheimer's disease

Neville, Jon; Kopko, Steve; Broadbent, Steve; Avilés, Enrique; Stafford, Robert; Solinsky, Christine M.; Bain, Lisa J.; Cisneroz, Martin; Romero, Klaus; Stephenson, Diane

doi:10.1016/j.jalz.2014.11.005

Cited by 36 publications

(41 citation statements)

References 17 publications

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“…Goals pursued by this group included improving decision-making in future trials, controlling access to shared study data, imputation of missing data, pooling data to improve analysis, and reproducing results [126][127][128][129][130][131].…”

Section: Analyzing Study Publicationsmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

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SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

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Section: Analyzing Study Publicationsmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

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“…Pooling data in this fashion allow analysts to query all trials or subsets of trials in the database without writing programming statements for each new study. The database is available to qualified researchers and currently consists of placebo clinical data from 24 trials of about 6500 subjects in total [5]. The CAMD AD database was fundamental to developing a drug-disease-trial model for mild-tomoderate AD, which became the first regulatory-endorsed quantitative clinical trial simulation tool.…”

Section: Public-private Partnershipsmentioning

confidence: 99%

Big data to smart data in Alzheimer's disease: Real‐world examples of advanced modeling and simulation

Haas¹,

Stephenson

Romero

et al. 2016

Alzheimer's & Dementia

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Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

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“…These datasets represent clinical and observational studies as well as clinical trials data held by pharmaceutical companies. Also discussed were strategies to integrate and provide access to these data to qualified researchers, as has been successful in CAMD's AD database [6].…”

Section: Modeling and Simulationmentioning

confidence: 99%

“…cdisc.org). CAMD used CDISC-AD data standards to build an online data repository currently comprising data on 6500 subjects from 24 AD clinical trials from nine companies [6]. This database was used to create the AD clinical trial simulation tool mentioned earlier [5].…”

Section: Data Sharing Standardization and Harmonizationmentioning

confidence: 99%