PERC Reviewer: Timothy Lynch MD
Objectives
To understand the issues related to sample size and to be able to estimate sample size needed for various research design situations.
At the end of this module, the participant will be able to:

Estimate the required sample size based on power calculations (for comparative study designs)

Estimate the required sample size based on confidence intervals (for descriptive study designs)

Understand how sample size is related to statistical analysis

Find resources (textbook and internet) to assist with sample size estimation

Assignments

In this module you will be asked to calculate the sample size for 6 situations. E-mail your answers to these questions to the Course Director.

For your research question, write up the Sample Size section of the methods. Be explicit in terms of your assumptions and how you determined your number. Include a paragraph where you determine the effect of doubling the sample size or halving it. How would you compensate for these changes? Would your results still be valid?

Readings
Main reference (required):

Hulley, SB, Cummings, SR, et al. (2001). Designing Clinical Research, Second Edition; Lippincott Williams and Wilkins. -- Chapter 6. Estimating the sample size Supplementary references (optional):
Cohen, Jacob (1977). Statistical Power Analysis for the Behavioral Sciences, Revised Edition; Academic Press. [A newer edition has been published]

This is the granddaddy of books on this subject. If you think this module is detailed, have a look at the book! If you can find a copy, read the discussion on small, medium and large effect sizes. This may help in your understanding of what constitutes clinically important effects. Kraemer HC, & Thiemann S (1987). How Many Subjects? Statistical Power Analysis in Research; Sage Publications.

One of the earliest books on the subject, it is only about 100 pages in length. I go back to this one regularly for help in explaining the issues in sample size estimation. Lipsey, Mark (1990). Design Sensitivity: Statistical Power for Experimental Research; Sage Publications.