if you REALLY want to know how I spend my Saturday nights... oh, and I did 2 loads of laundry while working on my research. Party animal, I think not, If I am understanding the difference between the t-test and ANOVA correctly, I would recommend using ANOVA for this particular study. As the text explained (if understood right), the t-test would lead to too much error if we used the amount of variables or subjects needed in a clinical study to accurately gauge a medications effect. The benefit of ANOVA is that even as many as 100+ variables can be considered, for example the study A Clinical Trial of the Effects of Dietary Patterns on Blood Pressure, 459 subjects were used, (Appel, 1997), a study of this size would lead to a greater degree of error if t-test were used, and would not be beneficial in determining efficacy or results of said medications. To determine which would be most beneficial however, I would have to have a better understanding of calculating actual error in numbers using the provided formulas. Regardless, ANOVA provides an alternative to t-test and z-test, allowing a greater number of subjects to be used, making the results of the study more accurate; the whole point of a study is to compare the relative effectiveness of two or more treatments on a common criterion and in a z-test or t-test. This is difficult because the group numbers are limited. Generally, the smaller the variation due to experimental error, the more efficient the trial . In turn, the experimental error may be reduced by introducing various kinds of controls or by increasing the sample size. Both methods are used to reduce experimental error, but allowing for a greater number of groups allows for more accuracy without the increased experimental error. Using the example of an actual study of the medication Celecoxib, I was actually able to somewhat answer these questions, and grasp the point of using ANOVA or any other method for that matter. Adenoma Prevention with Celecoxib (APC) Prevention of Spontaneous Adenomatous Polyps (PreSAP), are referenced in the study as primary (Solomon, 2006). This study would be considered a one tailed test because it determines how the medication affects the groups within the study. Blood pressure at baseline and 1 and 3 years and changes in blood pressure in both the APC and PreSAP studies are dramatic. The percentage of people with blood pressure measurements at years 1 and 3 were 89% and 76% in APC and 88% and 87% in PreSAP. The APC trial showed a statistically significant increase in mean systolic blood pressure compared with placebo at 1 year in both the 200-mg (difference, 2.0 mm Hg; P=0.04) and the 400-mg (2.9 mm Hg; P=0.005) -twice-daily dose groups and in both dose groups at 3 years (200 mg, 2.6 mm Hg; P=0.03; 400 mg, 5.2 mm Hg; P
Posted on: Sun, 06 Jul 2014 03:22:20 +0000
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