6.0 Data Analysis: In this study, the independent variables are treatment (octreotide, comparzine, demerol) and time (0, 30, 60 minutes). The dependent variable is pain measured on a numerical rating scale (1 to 10). The null hypothesis is that there will be no difference in pain between treatments. The alternative hypothesis is that there will be a greater decrease in pain with octreotide. The investigator expects the pain at baseline to be 6 +/- 2. A 30% difference in pain between treatments will be clinically significant. This is a 0.3 * 6 / 2 = 0.9 effect size. The appropriate test is a two-factor ANOVA (treatment, time) with repeated measures on one factor (time) followed by one-tailed t-tests corrected for multiple comparisons. Six post hoc comparisons on a one-tailed test are appropriate for this design, so we made a maximum Bonferroni correction of p = 0.05 / 6 = 0.0083 ~ 0.005. I used a look-up table based on employing the method of Kraemer and Thiemann (1988) to obtain an initial estimate of the sample size, which was confirmed with 1000 iterations of a Monte Carlo simulation until the power was between 80% and 85% with a level of confidence of 95%. According to this method, a sample size of 32 per group (96 total) is needed to detect a clinically significant difference with the desired level of confidence and power. If two treatments (octreotide, comparzine) are used, a sample size of 28 per group (56 total) is needed.