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Improving the operational efficiency of Phase 2 and 3 trials

Overview of attention for article published in Trials, July 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

6 tweeters


1 Dimensions

Readers on

11 Mendeley
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Improving the operational efficiency of Phase 2 and 3 trials
Published in
Trials, July 2016
DOI 10.1186/s13063-016-1465-3
Pubmed ID

Jitendra Ganju


The period toward the end of patients' participation in late stage blinded clinical trials is highly resource intensive for the sponsor. Consider first a Phase 3 trial. If the trial is a success, the sponsor has to implement the next steps, which might be filing for approval of the drug with the US Food and Drug Administration (FDA). To shorten the time interval between trial completion and submission of the package to the FDA, sponsors front-load as much work as is possible at risk. The approach is efficient if the trial succeeds but is inefficient if it fails. For a failed trial, the sponsor is unlikely to proceed with the plan that assumed success. Phase 2 trials are also at risk of being inefficient. Many activities, such as planning for drug interaction studies, thorough QT studies, or site selection for Phase 3 trials, are set in motion prior to completion of the Phase 2 trial. The work going on in parallel is wasted if the trial fails. The proposal to improve the efficiency is to let an independent entity provide the sponsor critical information at an earlier time necessary to reevaluate activities ongoing in parallel and external to the trial.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 27%
Other 1 9%
Lecturer 1 9%
Student > Ph. D. Student 1 9%
Student > Master 1 9%
Other 2 18%
Unknown 2 18%
Readers by discipline Count As %
Medicine and Dentistry 3 27%
Nursing and Health Professions 2 18%
Pharmacology, Toxicology and Pharmaceutical Science 1 9%
Business, Management and Accounting 1 9%
Agricultural and Biological Sciences 1 9%
Other 1 9%
Unknown 2 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
of 17,814,645 outputs
Outputs from Trials
of 4,687 outputs
Outputs of similar age
of 272,210 outputs
Outputs of similar age from Trials
of 1 outputs
Altmetric has tracked 17,814,645 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,687 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 272,210 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them