Evidence map›Paper›PMID 29696232›Full record

ArticleContemporary clinical trials communications2018

Quantifying and visualizing site performance in clinical trials.

Eric Yang, Christopher O'Donovan, JodiLyn Phillips, Leone Atkinson, Krishnendu Ghosh, Dimitris K Agrafiotis

Open access · goldAbstract read
In one paragraph

Article in Contemporary clinical trials communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
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

9 citing papers in PubMed, 17 citations in OpenAlex.

  1. Identifying and Deploying Benchmarks for Clinical Research at Sites.Therapeutic innovation & regulatory science · 2026
    Article
  2. Article
  3. Article
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  5. Review
  6. Article
  7. Article
  8. A late-binding, distributed, NoSQL warehouse for integrating patient data from clinical trials.Database : the journal of biological databases and curation · 2019
    Article
  9. A dimensional warehouse for integrating operational data from clinical trials.Database : the journal of biological databases and curation · 2019
    Article
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

6 authors at 1 institution in 1 country.

Eric YangCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Christopher O'DonovanCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
JodiLyn PhillipsCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Leone AtkinsonCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Krishnendu GhoshCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Dimitris K AgrafiotisCovance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Covance (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials.

methodsOur methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity.

resultsLeveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results.

conclusionsThe use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.

Indexed as

Alzheimer's diseaseClinical trial optimizationData visualizationInvestigator performanceSite performance

Identifiers

PMID29696232
PMCPMC5898563
OpenAlexW2788083662

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.