Evidence map›Paper›PMID 29067333›Full record

ArticleAlzheimer's & dementia (New York, N. Y.)2017

Accelerating drug development for Alzheimer's disease through the use of data standards.

Jon Neville, Steve Kopko, Klaus Romero, Brian Corrigan, Bob Stafford, Elizabeth LeRoy, Steve Broadbent, Martin Cisneroz, Ethan Wilson, Eric Reiman and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia (New York, N. Y.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Review
  3. Article
  4. The Role of fMRI in Drug Development: An Update.Advances in neurobiology · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Evaluating the Alzheimer's disease data landscape.Alzheimer's & dementia (New York, N. Y.) · 2020
    Article
  9. New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020
    Article
  10. Review
  11. New Frontiers in Parkinson's Disease: From Genetics to the Clinic.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jon NevilleCritical Path Institute, Tucson, AZ, USA.
Steve KopkoCDISC, Austin, TX, USA.
Klaus RomeroCritical Path Institute, Tucson, AZ, USA.
Brian CorriganDivision of Pharmacometrics, Pfizer Global Research and Development, Groton, CT, USA.
Bob StaffordCritical Path Institute, Tucson, AZ, USA.
Elizabeth LeRoyCDISC, Austin, TX, USA.
Steve BroadbentCritical Path Institute, Tucson, AZ, USA.
Martin CisnerozCollege of Medicine, The University of Arizona, Tucson, AZ, USA.
Ethan WilsonArizona State University, Tempe, AZ, USA.
Eric ReimanBanner Medical institute, Arizona State University, Phoenix, AZ, USA.
Hugo VandersticheleBiomarkable, bvba, Gent, Belgium.
Stephen P ArnerićCritical Path Institute, Tucson, AZ, USA.
Diane StephensonCritical Path Institute, Tucson, AZ, USA.

Funding

FDA HHS U18 FD005320
6 · The paper itself

Abstract

introductionThe exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results.

methodsThe development of the Clinical Data Interchange Standards Consortium (CDISC) AD therapeutic area data standard was a comprehensive collaborative effort by CDISC and Coalition Against Major Diseases, a consortium of the Critical Path Institute. Clinical concepts for AD and mild cognitive impairment were defined and a data standards user guide was created from various sources of input, including data dictionaries used in AD clinical trials and observational studies.

resultsA comprehensive collection of AD-specific clinical data standards consisting of clinical outcome measures, leading candidate genes, and cerebrospinal fluid and imaging biomarkers was developed. The AD version 2.0 (V2.0) Therapeutic Area User Guide was developed by diverse experts working with data scientists across multiple consortia through a comprehensive review and revision process. The AD CDISC standard is a publicly available resource to facilitate widespread use and implementation. DISCUSSION: The AD CDISC V2.0 data standard serves as a platform to catalyze reproducible research, data integration, and efficiencies in clinical trials. It allows for the mapping and integration of available data and provides a foundation for future studies, data sharing, and long-term registries in AD. The availability of consensus data standards for AD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among clinical trials, thereby improving our understanding of disease progression and treatment.

Indexed as

BiomarkersCDISCClinical outcome assessmentsClinical trialsData scienceData standardsDrug development and Alzheimer's diseaseMild cognitive impairmentRegulatory scienceTherapeutic Area User GuideTranslational science

Identifiers

PMID29067333
PMCPMC5651436

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.