Choosing the Right Design Overview
We’re working on this section. For now, have a look at:
- A good starting point when selecting outcomes is the COMET website, which collates information on standard outcome sets for trials.
- There are more PRECIS-2 resources online, as well as an e-book.
- Although the CONSORT Statement isn’t often used as a design tool, it should be. Knowing what you’ll need to report when the trial is finished is actually a pretty good place to start.
Top 5 Choosing the Right Design Tips
01.
It is entirely possible to design-in both inefficiency and irrelevance by not thinking through the consequences of design decisions for participants, user of the trial results and the trial team.
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The outcomes you select drive both relevance and much of the work involved in doing the trial. Outcome selection needs great care.
03.
One tool to help with matching design decisions to what the future users of the trial results want is PRECIS-2, which was published by Loudon and colleagues in 2015.
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More to come: keep checking the site…
More About Choosing the Right Design
Once you’ve chosen your research question you can move on to deciding on your trial design. When designing a study we should always seek the highest quality design that minimises bias, is feasible and will provide information that is relevant to those we are designing the trial for. Each trial design is different, and should be thought through in terms of, for example, the specifics of the population involved, the disease condition and the type of intervention. For example, we would always recommend a blinded trial design, but sometimes this isn’t possible due to the nature of the intervention – in these types of cases we can begin to look at blinding outcome assessors in an effort to ensure bias doesn’t creep into the results. As well as the research question itself, we must also take into account the available resources (staff, infrastructure and time), feasibility and ethical considerations of embarking on a randomised controlled trial.
The attrition of trial participants can impact negatively on the trial’s results. Bias can be introduced if participants drop-out as a result of experiencing severe side effects, and the study can be underpowered if attrition rates are substantial.
