the standardized difference in a study, the power and the significance level. From this nomogram, we can read that we need a few parameters to estimate the required sample size, i.e. Figure 1 shows an example of a nomogram for sample size estimation as published by Altman. In the case of a simple study design, such as our RCT on EPO treatment, a graphical method can be used to estimate the sample size required for the study. To determine how many patients we actually need to include in our RCT to detect a clinically relevant effect of EPO, we need to perform a sample size calculation or estimation. Intuitively, we expect that the more patients we include in our study, the more significant our difference will be. Of course, we hope to find a statistically significant difference in haemoglobin level between the group treated with EPO and the placebo group. After the intervention period, haemoglobin levels in the treated and placebo groups are compared. The primary outcome of this study is a continuous one, namely haemoglobin level. These patients are randomized to receive either EPO or placebo treatment. Suppose one wishes to study the effect of EPO treatment on haemoglobin levels in anaemic dialysis patients (haemoglobin <13 g/dl in men and <12 g/dl in women). The Basic Principles of Clinical Studies: An Example
#Types of statistical calculations for sample size trial
In this paper, we explain the basic principles of sample size calculations based on an example describing a hypothetical randomized controlled trial (RCT) on the effect of erythropoietin (EPO) treatment on anaemia in dialysis patients. Furthermore, these calculations are sensitive to errors, because small differences in selected parameters can lead to large differences in sample size. However, it is difficult for investigators to decide which method to use, because there are many different formulas available, depending on the study design and the type of outcome studied.
Determining the sample size is one of the first steps in the design of a trial, and methods to calculate the sample size are explained in several conventional statistical textbooks. If the sample size is too small, one may not be able to detect an important effect, while a sample that is too large may be a waste of time and money. Optimizing the sample size is extremely important. The main aim of sample size calculations is to determine the number of participants required to detect a clinically relevant treatment effect. The sample size is the number of patients or other experimental units that should be included in a study to be able to answer the research question.