1. Have an original idea to address an important problem
Has the proposed research been done before? If so, how is repeating it going to help?
Are you addressing a common problem or a rare but severe problem?
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2. Phrase the research question(s) clearly
Are you testing a hypothesis-if so state it?
If you are planning a descriptive study, say so, rather than dressing it up as a test of a hypothesis. Avoid "fishing expeditions"
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3. Justify the type of study design to answer the research question
Whatever the design chosen e.g. experimental, observational, descriptive, qualitative justify it and make sure you address the key design issues relevant to that type of study.
If its an intervention of any sort the gold standard should be an RCT. If so make sure you specify how you will randomise patients to the intervention and ensure concealed allocation and achieve blinding where this is possible. The Clinical Trials subcommittee of the Renal Association are prepared to advise on the design of RCTs. [Contact Dr David Jayne dj106@cam.ac.uk]
If you're not doing an RCT, say why not (it's recognised that RCTs are not appropriate or feasible for evaluating all interventions).
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4. Make it clear how you are going to recruit subjects and that this is feasible in the time and how you will gather data
The number of subjects required should be specified and justified for both quantitative and qualitative research (see below).
Make sure you will be able to recruit the numbers -allow for ineligibility, non-participation and loss to follow-up.
What measurement instruments are you going to use, and have they been validated?
Are the right outcomes being measured?
Are the methods reproducible in your hands?
Is the duration of follow-up appropriate for the outcomes you want to measure?
How will you take account of confounding factors (in measurement and/or analysis) and minimise bias in selecting subjects and obtaining information?
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5. Get the statistics right
If statistical analysis (e.g. designing prediction equations, survival analysis) is going to be a major part of your research, involve a statistician - not just cosmetically, but as part of the research team and from the planning phase
For quantitative studies, show a power calculation if testing a hypothesis test or precision of estimate if descriptive.
For qualitative studies, say how you know when you have reached data saturation (the point at which no new themes emerge).
Pilot studies still need some justification of numbers - use data from other studies to give an estimate of the size of the effect you are likely to see.
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6. Justify the involvement of the applicants
If you're planning multidisciplinary research, make sure you have real, not token, involvement of the relevant disciplines. Ensure you have the relevant expertise involved-e.g. statisticians, other methodologists as appropriate.
If you're planning to employ someone to do the research, make sure that the salary would enable you to employ someone qualified to do it - and explain how it will help the career of the individual you employ.
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7. Address the safety and ethics of the proposed research
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8. Describe the study carefully and honestly
Don't say you'll do things you won't be able to do - don't give "hostages to fortune". Don't be overambitious-remember feasibility is a key criterion.
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9. Get expert advice, and state that you've had it
If you're using complicated measurements, involve experts in their use
Get your application peer-reviewed internally, having obtained the published "criteria" or mission statement of your target funding source; if you've submitted the grant application before, keep and read the reasons and suggestions made in the rejection letter; and amend your application accordingly
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10. Specify how the results will translate into clinical practice
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