Rejection following peer review can mean a considerable amount of additional work for many authors to get their studies published. In the worst cases, their studies may be simply un-publishable. Much heartbreak and hard work can be avoided by simply planning and designing your study properly in advance. In the long run, this will save you time, allowing you to get on with the research for your next big paper.
No-one wants to have to repeat experiments because the controls were inappropriate or the case/sample numbers were insufficient to provide enough statistical power. Frequently though, researchers rush into experiments without making all the proper considerations, and this can result in delays when their manuscripts reach the peer review stage. Remembering a few basic principles of study design can help to reduce the risk of outright rejection and repeated experimentation.
1.Have a hypothesis or research question
Having a hypothesis or appropriate research question enables you to ‘frame’ your research within an appropriate context, which in turn will help you apply the appropriate controls. It will also help you describe the rationale for your study when it is time to write it up. Having a hypothesis also means that the objectives of the study are clearly defined, thus reducing the chance that your study will be open-ended and possibly criticised for being incomplete. You can then logically work through these objectives and, importantly, present your results in a logical manner rather than haphazardly.
2.Ensure that the appropriate methods are used
Once you have a clear idea of the aims of your study, and the specific research question you are setting out to answer, you will need able to determine what methods would be appropriate to achieve these. Important considerations include deciding whether subjective, qualitative data will be sufficient to address your question, or whether there is a need for more quantitative methods. For basic science studies, such considerations might include the following questions. Will the combination of RT-PCR and in situ data be enough, or is there a need for qPCR? Is Western blotting alone sufficiently sensitive or do you need to also perform immunohistochemistry and cell counting experiments to show a difference between groups? For clinical studies, important considerations include the choice of controls, sample sizes, statistical tests and approach, all of which are described in more detail in the points below.
3.Ensure that the appropriate controls are used
Controls are included in experiments to rule out alternative hypotheses. There’s an old saying that “nothing can be proven, only disproved”, and this is precisely why appropriate controls are necessary: to disprove any feasible alternative interpretations of the data you obtain and/or to eliminate or minimize the effects of extraneous variables. Consider what alternative hypotheses exist, and systematically rule them out by performing experiments that disprove them. There are generally two types of controls: positive and negative. Positive controls show that a negative result is not due to a failure of the experimental system. Negative controls provide an indication of the ‘background noise’ or baseline value with which to compare values from your experimental sample. In quantitative studies, a “relative control” or “housekeeping control” is required to show that changes in the apparent levels of a target gene or protein are not caused by differences in the amounts of protein or DNA in the sample. These levels can be used as a baseline to measure changes in relative levels of a target gene or protein. Common housekeeping molecules include β-actin and GAPDH. In clinical trials, subjects in a placebo group in intervention trials, and normal control subjects in observational trials, need to be matched as closely as possible to those in the treatment or disease group in terms of age, sex and numerous other potential confounding factors. In randomized controlled trials, accepted procedures for assignment to groups also need to be followed (see, for example, the ICH good clinical practice guidelines at: http://www.ich.org/LOB/media/MEDIA482.pdf).
4.Use sample sizes large enough to provide a definitive result
Many studies fail to achieve the desired impact or to fully support a given hypothesis because the effect is too small or the variability too large to show statistical significance. Often this can be simply overcome by increasing the sample size. However, once a study has been performed and the data analyzed, it can be impossible to go back and increase the numbers without starting all over again. For this reason, pilot studies are often performed in advance of larger scale studies. Talk to a statistician. Determine the size of the effect of your treatment and/or the variability in your population before starting large-scale studies, and use this information to determine the sample size required to give you statistical power. Doing this can save you time, money and potential disappointment later.
5.Use appropriate statistical tests to analyze your data
Statistical analysis of your data is essential to show that an effect is genuine and significant. Tests of significance demonstrate the robustness of your findings, essentially showing how unlikely it is that your findings were obtained ‘by chance’. Are your data continuous or discrete? Are they normally distributed or non-normally distributed? The nature of your data will determine how they should be analyzed and what tests are appropriate. If in doubt, consult a statistician who will be able to advise you on the most appropriate tests to use and what these tests indicate. Determining the right tests to use in advance will save you having to repeat your analyses if you got it wrong first time round, with the distinct possibility that no significant effect will be observed when the appropriate tests are used. For clinical trials, the following guidelines may be useful:
