Sample Size and Power Calculations

The sample size of a study is a key design and planning consideration. However, sample size and power calculations are often either poorly reported or not reported at all, which suggests they may not form a routine part of study planning. Inadequate understanding of sample size and statistical power can result in poor quality studies. Journals increasingly require a justification of sample size, for example through the use of reporting checklists. However, for meaningful improvements in research quality to be made, researchers need to consider sample size and power at the design stage of a study, rather than at the publication stage. Here we briefly illustrate sample size and statistical power in the context of different research questions and how they should be viewed as a critical design consideration.

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SPCalc: A web-based calculator for sample size and power calculations in micro-array studies

Bioinformation ◽

10.6026/97320630001251 ◽

2006 ◽

Vol 1 (7) ◽

pp. 251-252 ◽

Cited By ~ 5

Author(s):

Weiliang Qiu ◽

Mei-Ling Ting Lee

Keyword(s):

Sample Size ◽

Web Based ◽

Power Calculations ◽

Micro Array

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Understanding between-cluster variation in prevalence and limits for how much variation is plausible

Statistical Methods in Medical Research ◽

10.1177/0962280220951831 ◽

2020 ◽

pp. 096228022095183

Author(s):

Mark D Chatfield ◽

Daniel M Farewell

Keyword(s):

Standard Deviation ◽

Sample Size ◽

Correlation Coefficient ◽

Coefficient Of Variation ◽

Binary Data ◽

True Cluster ◽

Power Calculations ◽

Log Odds ◽

Cluster Variation ◽

Cluster Correlation

In clinical trials and observational studies of clustered binary data, understanding between-cluster variation is essential: in sample size and power calculations of cluster randomised trials, for example, the intra-cluster correlation coefficient is often specified. However, quantifications of between-cluster variation can be unintuitive, and an intra-cluster correlation coefficient as low as 0.04 may correspond to surprisingly large between-cluster differences. We suggest that understanding is improved through visualising the implied distribution of true cluster prevalences – possibly by assuming they follow a beta distribution – or by calculating their standard deviation, which is more readily interpretable than the intra-cluster correlation coefficient. Even so, the bounded nature of binary data complicates the interpretation of variances as primary measures of uncertainty, and entropy offers an attractive alternative. Appealing to maximum entropy theory, we propose the following rule of thumb: that plausible intra-cluster correlation coefficients and standard deviations of true cluster prevalences are both bounded above by the overall prevalence, its complement, and one third. We also provide corresponding bounds for the coefficient of variation, and for a different standard deviation and intra-cluster correlation defined on the log odds scale. Using previously published data, we observe the quantities defined on the log odds scale to be more transportable between studies with different outcomes with different prevalences than the intra-cluster correlation and coefficient of variation. The latter increase and decrease, respectively, as prevalence increases from 0% to 50%, and the same is true for our bounds. Our work will help clinical trialists better understand between-cluster variation and avoid specifying implausibly high values for the intra-cluster correlation in sample size and power calculations.

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