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If you feel unready to get started then just put “TBD” for “to be determined” for sections currently unknown, and prefacing sections that are still under development with “(under development)”.  Then it can be shared which will foster collaboration as early in the process as possible.

After clicking “submit”, invite others to collaborate, and work toward research-design-excellence, rather than mere research-design-completion.  Inviting others in on the design phase, before it’s fully “ready”, is a great way to get key players on board, improve buy-in, and improving study design before it’s too late to make design changes.

  • RECOMMENDATION:

    Enter only the items with a red star for now and press submit. Then after you receive an email to validate your account you can edit the other sections, then invite your collaborators to assist in the design of the study.

  • We recommend you include a unique identifier so you can easily find it with a search
  • Please provide any details necessary to do the research: Protocols, dosage, frequency, special instructions
  • Enter which grants, if any, that you will be pursuing, as well as other funding sources
  • List all diseases, illnesses, health conditions, symptoms for which this study is intended to impact.
  • Persons Affected

    The total number of persons affected with a condition that may benefit if the hypothesis is true.

  • in the US
  • in the world
  • Recommendation: Make at least one of these based on directly measurable human-function indicators (example: normal-cell survival, QLQ-c30), in addition to indirect diagnostic measurements (ex: CBC analytics). Save the diagnostic measurements for secondary endpoint measurements.

    Caution: Avoid using primary metrics that may indicate negative response in spite of providing an overall benefit to the patient. This can happen when an incorrect pathology is presumed, or when the metric is too specific. In this matter it is actually preferred to measure, for example in the case of SARS, days until oxygen sats are back above 93, or post recovery lung capacity %, as opposed to patients died (which may require an unreasonably large population to see a statistically significant effect), or as opposed to a metric associated with a specific presumed pathology, or a metric with a lot of variability, like CRP count which is both pathology associated and which may be subject to time and day in the infection when the measurement is taken. Select a metric that is easily, consistently, and reliably taken, with as few uncontrolled factors as possible. Other metrics can be, and should be, employed, but they should be secondary in nature (next field).

    The reason for making these more tenuous metrics as secondary metrics is that if a primary metric fails the whole study is generally reported as failing despite the fact that the patients in the experiemental group may live 20% longer and with fewer complications. Choosing the wrong metric as a primary metric is an effective way to administer a study designed to bring about a failed response regardless how efficacious the treatment is.
  • These metrics can often be more instructive and beneficial to understanding exactly what is happening to the patient, but may also have a more tenuous relationship with the outcome than might be presumed. In the past, the specific and scientific nature of these metrics have tempted researchers to use these as the primary metrics by which to judge the efficacy of a treatment or protocol, despite that it frequently turns out they are not as directly related to the patient recovery as presumed at this stage of the process. Using these criteria as primary metrics often causes serious errors in the peer assessment of medical research and can trickle down to erroneous practical application, resulting in less effective care and less ideal patient outcomes.
  • Please include studies related to this study that demonstrate presumed efficacy of treatment. Provide citations to peer reviewed journals (AMA format is preferred), and PMID with links whenever possible. Separate each with a blank line.
  • Include the treatments the provide the best outcomes, as well as those which are most cost effective. This includes conventional treatments as well as other treatments current under investigation.
  • Cost per patient

    Total cost for this treatment, per patient. This should include clinical costs as well as drug and medical technology costs.

  • Minimum
  • Typical
  • Maximum
  • Treatment Savings Per Patient, if any

    The expected savings per patient for this treatment compared to standard care. This should include clinical costs as well as drug and medical technology costs.

  • Minimum
  • Typical
  • Maximum
  • Include all potential life-threatening contraindicating conditions, as well as the metrics and associated limits for participating in the treatment. These metrics must be intuitively objective, not subject to interpretation.
  • If this experiment is designed to validate the claims of a specific protocol that has previously been tested, please indicate the person(s) who designed and/or administered the protocol, who might be consulted to verify that the exact protocol is followed in this validating study. If no such person(s) exist, enter your own name in this section.
  • * The "CF Study-Validation Verification" form ensures that everything, from timing, to dose, to patient criteria, to things like hospital food, contraindications, and a range of doses, etc, are all reasonable and are within the parameters most likely to demonstrate efficacy.
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