Sample size estimation for the sorcerer's apprentice. Guide for the uninitiated and intimidated.
OBJECTIVE
To review the importance of and practical application of sample size determination for clinical studies in the primary care setting.


METHODS
A MEDLINE search was performed from January 1966 to January 1998 using the MeSH headings and text words "sample size," "sample estimation," and "study design." Article references, medical statistics texts, and university colleagues were also consulted for recommended resources. Citations that offered a clear and simple approach to sample size estimation were accepted, specifically those related to statistical analyses commonly applied in primary care research.


RESULTS
The chance of committing an alpha statistical error, or finding that there is a difference between two groups when there really is none, is usually set at 5%. The probability of finding no difference between two groups, when, in actuality, there is a difference, is commonly accepted at 20%, and is called the beta error. The power of a study, usually set at 80% (i.e., 1 minus beta), defines the probability that a true difference will be observed between two groups. Using these parameters, we provide examples for estimating the required sample size for comparing two means (t test), comparing event rates between two groups, calculating an odds ratio or a correlation coefficient, or performing a meta-analysis. Estimation of sample size needed before initiation of a study enables statistical power to be maximized and bias minimized, increasing the validity of the study.


CONCLUSIONS
Sample size estimation can be done by any novice researcher who wishes to maximize the quality of his or her study.
Ray
J G
JG
Department of Medicine, Women's College Hospital, Ont. rayjg@fhs.mcmaster.ca
Vermeulen
M J
MJ
D006801:Humans; D011320:Primary Health Care; D011786:Quality Control; D012107:Research Design; D018401:Sample Size