Computation of Sample Size for Clinical Studies

The estimation of sample size is an essential element in the design of clinical research, directly affecting the study’s validity and statistical power. This chapter examines the fundamental concepts of sample size determination in clinical research, highlighting the equilibrium between under-sampling and over-sampling. It delineates essential aspects influencing sample sizes, such as effect size, variance, alpha and beta errors, and their impact on identifying clinically significant differences. The chapter examines two primary methodologies for sample size calculation: the precision approach, emphasizing confidence intervals, and the power approach, concentrating on identifying a certain impact size. Practical examples are shown for several research designs, including unpaired and paired means, proportions, and correlation analyses. Furthermore, it incorporates R programming tools such as the pwr and TrialSize packages for efficient and precise sample size calculation. Real-world problems for sample size calculation are illustrated through coding examples. Ultimately, the content provides researchers with the expertise to determine ideal sample sizes, hence ensuring sufficient study power and resource efficiency in clinical research, including clinical trials.