Randomized controlled trial (RCT) is considered as the gold standard for evaluating intervention or health care. Compared with an observational study, randomization is an effective method to balance confounding factors between treatment groups and it can eliminate the influence of confounding variables. When a research investigator wants to design a clinical trial, the key consideration is to know how many participants are to be added to the sample to obtain significant results for the study. Even the most rigorously executed study may fail to answer its research question if the sample size is too small. On the other hand, study with large samples will be more difficult to carry out and it will not be cost effective. The goal of sample size estimation is to calculate an appropriate number of subjects for a given study design (1).

Parallel RCT design is most commonly used, which means all participants are randomized to two (the most common) or more arms of different interventions treated concurrently.

Assuming RCT has two comparison groups and both groups have the same size of subjects; sample size calculation depends on the type of primary outcome measures.

Problem: The research question is whether there is a difference in the efficacy of mirtazapine (new drug) and sertraline (standard drug) for the treatment of resistant depression in 6-week treatment duration. A ll parameters were assumed as follows: p =0.40; p₀=0.58;α=0.05;β=0.20; δ=0.18; δ₀=0.10.

Problem: The research question is whether there is a difference in the efficacy of A CE II antagonist (new drug) and A CE inhibitor (standard drug) for the treatment of primary hypertension. Change of sitting diastolic blood pressure (SDBP, mmHg) is the primary measurement, compared to baseline. All parameters were assumed as follows: mean change of SDBP in new drug treatment group=18 mm Hg; mean change of SDBP in standard treatment group =14 mm Hg;α=0.05;β=0.20; δ=4 mmHg; δ₀=3 mm Hg; s=8mm Hg.

Indeed, the steps for calculating sample size mirror the steps required for designing a RCT. Firstly, the researcher should specify the null and alternative hypotheses, along with the type I error rate and the power (1- type II error rate). Secondly, the researcher can gather the data of relevant parameters of interest but sometimes a pilot study may be required. Thirdly, the sample size can be estimated based on several reasonable parameters. In fact the key point which readers need to know is about the choice of null and alternative hypothesis, which should be adjusted according the study objective. Some readers might encounter obstacle in the determination of non-inferiority/equivalence/superiority margin. This parameter has clinical significance, which should be cautiously determined and it must be reasonable. Sometimes, if δ is too large, several inefficacious drugs will appear in the market for they can be judged as non-inferiority/equivalence; On the contrary, if δ is too small, some potential useful drugs will be neglected. In short, the choosing of δ is based on the explicit discussion of clinical experts and statisticians, not only depends on statisticians’suggestion. The other important thing to remember is that when δ is determined finally, it cannot be changed (7).

This paper gives simple introduction of principals and methods of sample size calculation. A researcher can calculate the sample size given the types of design and measures of outcome mentioned above. It also provides some knowledge on what information will be needed when coming to consult a biostatistician for sample size determination. If someone is interested in designing a non-inferior- ity/equivalence/superiority RCT, a consultation from a biostatistician is recommended.