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Designing randomized clinical trials in surgery

Randomized clinical trials (RCTs) are considered the most powerful tools for generating scientific evidence in modern clinical research. The key distinguishing feature of this type of study is the random allocation of participants to treatment arms. This process tends to produce homogeneous groups by eliminating both conscious and unconscious selection bias as well as balancing the groups with respect to known and unknown confounding variables. Randomization in this way, however, is not enough to ensure reliable and clinically useful results. Other design elements, frequently misunderstood by surgeons, strongly affect the clinical relevance of trial findings. One of the most important of these elements is the choice of the minimum treatment effect of interest for the sample size calculation. Sample size evaluation is almost always a matter of compromise between ideal statistical power and available resources. Achieving balance between these two aspects can be challenging. Calculating the sample size for trials with dichotomous outcomes requires four basic components: the size of type I error α (usually set at 0.05), which is the probability that the difference observed between study arms could occur by chance alone; the size of type II error β (usually set at 0.20–0.10) or, more commonly, the power of the study (1 − β), which refers to the probability that two treatments do not differ when a difference of given magnitude does actually exist; the event rate in the control group, usually retrieved from the available literature; and, finally, the treatment effect size of interest ( ), which is the minimal difference in outcome that the study was designed to detect. The latter parameter markedly affects the required sample size, being related to the needed number of patients by an inverse quadratic relationship (halving the effect size involves a greater than fourfold increase in the sample size). Determining the treatment effect size of interest reflects both clinical acumen and the potential public health effect. Therefore, this not a job for statisticians in isolation. Although there is no right answer to the question, ‘How large should the effect size of interest be in a RCT?’, clinical judgement, experience with the problem being investigated and the context of research allow for the selection of a suitable value. For example, consider a trial comparing the efficacy of two cancer treatments in terms of disease recurrence. The literature indicates an estimated event rate of 10 per cent among patients receiving the control treatment. In this setting, an absolute 5 per cent reduction in the relapse rate is worth detecting. Conversely, the same effect size may be of little interest in a study investigating a new and costly drug for reducing transient cough associated with acute upper respiratory tract infection if the control drug has a success rate of 60 per cent and is less expensive. The aim of the study is another factor affecting the choice of effect size of interest and, ultimately, the required sample size. In a superiority trial, the objective is to test whether the outcome of interest in the experimental arm is, in some defined way, better than the control arm. This classical design is widely used when there is no effective therapy, or when available information suggests that the test treatment may be more effective than conventional therapy1. In non-inferiority or equivalence trials, the objective is simply to establish whether outcome in the experimental treatment is no worse than or equivalent to the reference therapy by a predefined acceptable amount. These trials have gained popularity in recent decades as useful research tools in situations where a placebocontrolled design is considered unethical (for example, in the treatment of patients with myocardial infarction or cancer) or when the treatment being tested is not expected to be better than the standard intervention in terms of efficacy, but has other advantages, such as less invasiveness, fewer adverse effects, greater availability, reduced costs or greater ease of administration2,3. Superiority trials are usually designed to detect larger differences than non-inferiority or equivalence trials are designed to rule out. Logically, a non-inferiority or equivalence margin should be smaller than a superiority margin, because the former is compatible with equality between treatments, whereas the latter excludes equality4. Of note, in a trial that investigates non-inferiority, the question of interest is not symmetrical. The position of the upper confidence limit is not taken into consideration3. According to this, a one-sided statistical test is used to test only the null