PROTEUS
 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 protein-poor 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% seizure-free 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.