ReviewOpen forum infectious diseases2022
The Value of the Information That Can Be Generated: Optimizing Study Design to Enable the Study of Treatments Addressing an Unmet Need for Rare Pathogens.
Review in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In traditional phase 3 trials confirming safety and efficacy of new treatments relative to a comparator, a 1-sided type I error rate of 2.5% is traditionally used and typically leads to minimum sizes of 300-600 subjects per study. However, for rare pathogens, it may be necessary to work with data from as few as 50-100 subjects. For areas with a high unmet need, there is a balance between traditional type I error and power and enabling feasible studies. In such cases, an alternative 1-sided alpha level of 5% or 10% should be considered, and we review herein the implications of such approaches. Resolving this question requires engagement of patients, the medical community, regulatory agencies, and trial sponsors.
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Registered trials
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.