ueg education used in this regard, though other methods also exist.20 Of note, certain projects have more than one outcome. In these cases, the primary outcome and any secondary outcomes should be clearly predefined, and the analysis should be performed accordingly. Finally, important discoveries can still be made outside those predicted in the analysis plan or even by the original question. In these cases, it is important to realize whether the findings are indeed a true signal or an artefact from multiple comparisons. Additional confirmatory studies, focusing on the new question, may be needed. Mistake 6 Conducting the study on the wrong number of participants Fundamental to conducting a good study is the awareness of sample size (or study size).21 If an effect truly exists then the study should be performed on a sufficiently large sample to detect it. Too small a sample size can also increase the likelihood of generating a false-positive effect because smaller sample sizes can be more prone to certain biases (see mistake 7). What the sample size should be for any particular study depends on a number of parameters. There are several pieces of knowledge that are fundamental to all sample size calculations (Figure 3). The first consideration is the prevalence of the disease in the unexposed population, which may be known from previously conducted research or from other similar populations. There is a level of guestimation involved in determining this parameter, but basing it on prior knowledge is, of course, beneficial. The second factor is the minimum effect size, that is, the clinically relevant difference that we wish to detect (e.g. a reduction of bilirubin levels of 1mg/dL or 0.1mg/dL). The smaller the difference to be detected, the larger the sample size required. The third parameter is the significance level (or alpha value) of the study, which is the probability of rejecting a null hypothesis given that it is true (type-I error) and is frequently set at 5%. The final component when calculating sample size is the power of a study, the probability that it will detect an effect when there is one to be detected-power is frequently set at 80% or 90%. With these parameters known to us or chosen, the required sample size can be calculated. Other factors that may need to be taken into consideration include the ratio of controls to cases and the likelihood of loss to follow-up in the patient population. The more complicated the study design, the more complicated the calculation of sample size becomes. Whilst there are several statistical packages available that allow the calculation of sample sizes (some opensource and others proprietary),22 as with so many aspects of study design it is best to ensure 22 Mistakes in... 2020 What is the prevalence of the disease in the unexposed population? What is the significance? ? What is the minimum eﬀect size you wish to be able to detect? What is the power? Figure 3 | Sample size considerations. you have the correct skills mix and support for your project from the outset. Consulting or collaborating with a statistician at this stage of project development can be extremely beneficial. In practice, sample size considerations are often based as much, if not more, on resource implications as they are on the purely mathematical components mentioned above. If we know there is a finite number of cases, an adjustment to other parameters will have to be justified to allow the study to go ahead, such as increasing the numbers of controls to cases, or increasing the effect size to be detected. Conducting a study on too many cases (perhaps by wishing to detect a very small effect size) may be considered a waste of resources and, therefore, the study runs a higher risk of not being funded (see mistake 8). Mistake 7 Not considering potential sources of bias from the outset The avoidance of bias is one of the cornerstones of good epidemiologic study design. Bias has been descrided as "... the deviation of results or inferences from the truth or processes which lead to such deviation."23 There are a huge number of defined biases and it is not possible to discuss every single one of them here. Broadly, bias in epidemiological studies can be categorized into selection bias and misclassification (information bias; see table 1). Selection bias arises when there is a systematic difference in the characteristics of people within a study compared with those who are not. This can include differences or errors in those enrolled in the study in the first place compared with the target population (sampling bias), through the inclusion of an inappropriate control population (e.g. choosing hospital controls who have a particular condition), and through following up participants differently dependent on their exposure status (attrition bias or follow-up bias). Misclassification is a deviation in the measurement of one or more factors of interest in a study. Misclassification may affect all groups equally (nondifferential misclassification) or, perhaps more frequently, it may be more likely to occur in one group than another (differential misclassification, or information bias). Information bias includes observer bias (where a study team member differentially records observations based on known exposure or outcome status), recall bias (where a participant is more or less likely to remember accurately an exposure based on their outcome status) and reporting bias (where a participant is more or less likely to reveal or suppress certain information, particularly when there may be stigma attached to it [e.g. alcohol use]). We also include confounding in this section. There is debate as to whether confounding is strictly speaking a type of bias because it does not arise from a systematic error in the design of a piece of research, but rather from the nonrandom distribution of risk factors in the population being studied. A confounder is a third variable that is associated both with the disease and exposure of interest in any given study. If a confounding variable has been measured then it is usually possible to adjust for it in the analytical stages of the study through stratification or modelling. The study design can also be adjusted to minimize or remove confounding by restricting the study population or matching the population based on certain known (or suspected) confounding factors. If this is not possible or appropriate, careful thought as to the likely confounding variables in any study will ensure that appropriate data have been collected to allow adjustment at the analysis stage. Mistake 8 Undertaking unfeasible projects Resources for research are often limited, especially for those in the early stages of their career. Research that requires recruitment of patients is often orders of magnitude more expensive than research that can be performed on data collected via other mechanisms. Of course, although funding is a crucial point, it is not the only one, and once a research question has been framed, and a protocol has been finalized, it is important to evaluate whether the proposed project is feasible or not. At times, a protocol that on paper seems perfectly tailored to answer a research question is not practicable and will be impossible to execute in the real world. In such cases, the protocol and research question should be revisited, and a more practical approach considered. Although this may be frustrating because plenty of time and work will have already been invested in the project, it is vital to recognize unfeasible projects, and identify them as 'nonstarters'. This course of action may ultimately be more helpful to the researcher's professional development than the alternative of starting a neverending project. Many factors may render a project unfeasible. Insufficient protected time for carrying out the research, and an insufficiently framed research question are pitfalls that early-stage researchers should avoid. Finding

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