Randomized controlled trials (RCTs) are an experiment-based research technique. In the field of medical research, RCTs are highly valued as a "gold standard" of experimentation. In the increasingly popular field of evidence-based policymaking (EBPM) as well, RCTs are respected as the technique that can evaluate policy effects most accurately.
At a glance, RCTs may appear to be difficult to understand and/or perform. However, unlike advanced mathematical analysis techniques used by statisticians and econometricians, RCTs are simple in concept and not much different in principle from what is taught in science classes at elementary and junior high schools. In control experiments conducted in classrooms, children are taught that only one factor may be varied while other factors must be kept constant. For example, when we examine what conditions are suitable for the growth of soybeans, various factors, such as the presence or absence of light, water and oxygen and the temperature level, must be taken into consideration. In this case, before starting the experiment, we create two groups—one where soybeans are exposed to light and the other where they are unexposed—while the conditions of factors other than light are kept the same between the two groups, and we check whether soybeans grow in only either or both of the groups.
RCTs apply the same principle to various human activities. For example, when we want to know what kind of activity is effective in preventing elderly people from falling into a state requiring nursing care, there may be several possible candidates, such as physical workout, tai chi martial arts exercise, regular walking, massive intake of protein, and active communication with other people. In an RCT, people are divided into two groups, one of which engages in one of the candidate activities while the other does not, and it is checked several years later whether there is a significant difference between the two groups in the proportion of people who have fallen into a state requiring nursing care. For example, several hundred healthy elderly people may be selected as sample subjects and be divided into a group which is asked to practice tai chi exercise and a group which is not, and it is checked over several years whether a significant difference arises between the two groups in the proportion of people who have fallen into a state requiring nursing care.
How to form the two groups is critically important. If the group division is made in accordance with sample subjects' own preference—whether or not they prefer to practice tai chi—a variable factor other than the presence or absence of the effects of tai chi exercise may affect the difference between the two groups. For example, if people wishing to practice tai chi have strong health consciousness, their approach to physical exercises in general and eating habits may also affect the possibility of falling into a state requiring nursing care. This means that the premise of control experiments—that only one factor may be varied—falls apart. To avoid this problem, an RCT uses the randomization process for the formation of two groups. This means that whether a sample subject is assigned to one or the other of the two groups depends on sheer chance. For example, the assignment may be determined by dice rolls, with people who get an even number assigned to the tai chi group and people who get an odd number assigned to the non-tai chi group. To maintain randomization, the group division determined by chance must remain unchanged throughout the trial.
In practice, RCTs require several procedures. In the case of RCTs in the medical field, it is essential to follow the CONSORT guidelines, gain approval from the ethics committees of universities or research institutions, obtain prior online registration, and secure informed consent from trial participants. Although complex analysis techniques are not necessarily required, it is desirable to leave it to statistical experts to determine the number of trial participants (sample size) and analyze data from the trial results. However, most of the necessary procedures do not require complex mathematics. Even though support from statistical and other experts may be necessary in some respects, the success or failure of RCTs hinges on the administrative aspects of the trial, such as securing a sufficient number of participants and overseeing process management.