

NOMOGRAM FOR POWER ANALYSIS: notes on how to use....................
Using the nomogram for sample size calculations (a) continuous data, two independent groups. For example: Suppose that prior to launching a feeding supplement program among children in a malnourished area, we are designing a trial among laboratory rats of the supplement to see if an increase in body mass is achievable. Rats are to be randomly assigned into two groups: a proteinpoor diet (estimated to be equivalent to children's diet in the test region, and a diet with the enhanced protein content. We know that rats from 2 weeks to 12 weeks should increase by 280 g with a standard deviation of 35 g. We anticipate that the supplement will increase weight gain by additional 20 g by week 12. We want a high probability of detecting this increase so we set the power to 90% and a 5% significance level. METHOD: Need: Standard deviation of the variable (S) Clinically relevant difference or effect (δ) Significance level (α) Power (1 ß ) "Standardized difference" is Clinically relevant difference or effect (δ) = 20 = 0.57 Standard deviation (S) 35
Use the nomogram with StdDiff: 0.57, power: 90% (0.90), and α as 0.05 to obtain n = 130 or 65 in each group.
(b) categorical data (binary: two groups) For example: A new electrical stimulation device for epileptics is being tested. Patients are able to give themselves a mild, regulated electrical stimulus immediately they feel a seizure might be imminent. Twenty percent (20%) of these patients could expect to be free of seizures in a 12 month period. The device would be considered a success if that rate could be doubled to 40% seizurefree in a year, and we want a 85% probability of finding this result if it exists (at 99% significance). METHOD: Need: The expected proportion with the outcome in each group (p1 and p2) Significance level (α) Power (α) "Standardized difference" is _ p1  p2 = 0.4  0.2 = 0.2 = __0.2_ = 0.44 √(1) √0.3(0.7) √0.21 0.458 (where is the mean of p1 and p2)
Use the nomogram with StdDiff: 0.44, power: 85% (0.85), and α as 0.01 to obtain n = 260 or 130 in each group.
