Sample size calculation: Basic principles

Nephron Clinical Practice, 2011

The sample size is the number of patients or other experimental units that need to be included in a study to answer the research question. Pre-study calculation of the sample size is important; if a sample size is too small, one will not be able to detect an effect, while a sample that is too large may be a waste

Two of the most important questions related to all research studies are the way of selecting subjects and the number of subjects required for the study. Why are these two issues given so much importance? Let us take a case of a randomized controlled trial (RCT) for the treatment of hypertension and try to understand this. In an RCT, to show a difference between two drugs used for the treatment of hypertension, the researchers randomized hypertensive patients into two groups. Both the groups were given treatment and were evaluated at the end of the study to compare the desired outcome of reduction in blood pressure below a particular level. Suppose they get an inconclusive result in the study, they would have advocated against the use of this new drug. One issue relating to the extrapolation of this result is the size of the sample from which the results have been generated. If the result is generated from a large sample then often the results will be close to the truth provided the ...

Nephrology Dialysis Transplantation, 2010

Although most statistical textbooks describe techniques for sample size calculation, it is often difficult for investigators to decide which method to use. There are many formulas available which can be applied for different types of data and study designs. However, all of these formulas should be used with caution since they are sensitive to errors, and small differences in selected

This paper is designed as a tool that a researcher could use in planning and conducting quality research. This is a review paper which gives a discussion of various aspects of designing consideration in medical research. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for survey type of studies, observation studies and experimental studies based on means and proportions or rates, sensitivityspecificity tests for assessing the categorical outcome are presented in detail. Over the last decades, considerable interest has been focused on medical research designs and sample size estimation. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

Sample size determination is often an important step in planning a statistical study-and it is usually a dif cult one. Among the important hurdles to be surpassed, one must obtain an estimate of one or more error variances and specify an effect size of importance. There is the temptation to take some shortcuts. This article offers some suggestions for successful and meaningful sample size determination. Also discussed is the possibility that sample size may not be the main issue, that the real goal is to design a high-quality study. Finally, criticism is made of some ill-advised shortcuts relating to power and sample size.

Revista portuguesa de cardiologia : orgão oficial da Sociedade Portuguesa de Cardiologia = Portuguese journal of cardiology : an official journal of the Portuguese Society of Cardiology, 2003

In order to be valid, clinical studies must be methodologically rigorous. The internal validity of a study is of crucial importance: a study is valid if its results are an unbiased estimation of the true result. In this case, the validity is internal because it refers to the group of patients under study and not necessarily different ones (external validity or applicability). Internal validity in clinical research is achieved through rigorous design, data collection and appropriate analysis, and is threatened by bias (systematic errors) or chance (random variation of the phenomena under study). Regardless of the type of study (analytic, descriptive, etc.), the characteristics of its sample are fundamental for the validity of the results. The sampling methods are crucial if the study patients are to be representative of the population to which one desires to extrapolate the results. One of the most fundamental characteristics of a sample is its size. Even the best executed study may ...

Black Sea Journal of Health Science, 2021

The approval of local ethics committees is required for clinical researches. In order to obtain approval, how the sample size is determined, whether power analysis is done or not and under what assumptions these analyses are made, are important questions/problems. In hypothesis tests, it is possible two types of errors (type 1 error denoted by α and type 2 error denoted by β), of which α is the probability of rejecting the null hypothesis that is actually true and is the probability of accepting the actually false null hypothesis. These errors also determine the reliability of the test (1-α) and the power of test (1-β). While α is directly determined by the researchers and generally as taken 0.05 (in some cases 0.01), β cannot be determined directly. Because β, hence the power of test (1-β) depends on the α (negatively correlated with β) the variation in the population (positively correlated with β) and sample size (n; negatively correlated with β). In clinical researches, it is required that β does not exceed 0.10 (in some cases 0.05) so the power of test should be at least 0.90 and above. In this study, the sample sizes required for some statistical tests (independent sample t-test, oneway ANOVA and Chi-square) which are widely used in clinical research, were calculated with the G*Power program and some evaluations were made. As a result, as expected in the statistical tests, it was observed that decreasing both α and effect size and increasing the power of the test significantly increased the required sample size. However, it was also observed that increasing effect on the sample size of increasing the power of test decreased (5-11%) in the smaller values of α in the independent sample t-test, decreased (nearly 5%) when increasing the number of compared groups in one-way ANOVA and decreased (10-15%) when increasing degree of freedom of Chi-square test.

Medical science, 2014

It is mandatory to calculate sample size in any type of research in medical science to get a generalized conclusion for the population under study. If the study is well designed with a desired sample size then the standard error will be less and the power and precision will be good. All statistical procedures become valid in this context.

Calculation of exact sample size is an important part of research design. It is very important to understand that different study design need different method of sample size calculation and one formula cannot be used in all designs. In this short review we tried to educate researcher regarding various method of sample size calculation available for different study designs. In this review sample size calculation for most frequently used study designs are mentioned. For genetic and microbiological studies readers are requested to read other sources.

Biochemia medica, 2021

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.

Journal of advanced …, 2004

How many do I need? Basic principles of sample size estimation Background. In conducting randomized trials, formal estimations of sample size are required to ensure that the probability of missing an important difference is small, to reduce unnecessary cost and to reduce wastage. Nevertheless, this aspect of research design often causes confusion for the novice researcher. Aim. This paper attempts to demystify the process of sample size estimation by explaining some of the basic concepts and issues to consider in determining appropriate sample sizes. Method. Using a hypothetical two group, randomized trial as an example, we examine each of the basic issues that require consideration in estimating appropriate sample sizes. Issues discussed include: the ethics of randomized trials, the randomized trial, the null hypothesis, effect size, probability, significance level and type I error, and power and type II error. The paper concludes with examples of sample size estimations with varying effect size, power and alpha levels.

2009

Sample size calculations should be an important part of the design of a trial, but are researchers choosing sensible trial sizes? This thesis looks at ways of determining appropriate sample sizes for Normal, binary and ordinal data.