Sample Size Calculation in Clinical Studies: Some Common Scenarios

Mohammad Z. I. Chowdhury; Khokan C. Sikdar; Tanvir C. Turin

doi:10.6000/1929-6029.2017.06.04.3

Authors

Mohammad Z. I. Chowdhury Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
Khokan C. Sikdar Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
Tanvir C. Turin Department of Family Medicine, University of Calgary, Calgary, AB, Canada

DOI:

https://doi.org/10.6000/1929-6029.2017.06.04.3

Keywords:

Sample Size Calculation, Power, Hypothesis Test, Level of Significance, Mean, Proportion.

Abstract

Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible.

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