As we have seen, several summary measures of central tendency can be used for continuous outcomes. The most common of these measures is the mean. In clinical trials we calculate sample statistics, and these serve to estimate the unknown population means. When developing a new drug, the estimated treatment effect is measured by the difference in sample means for the test treatment and the placebo. If we can infer (conclude) that the corresponding population means differ by an amount that is considered clinically important (that is, in the positive direction and of a certain magnitude) the test treatment will be considered efficacious. [Pg.147]

In Chapter 10 we saw that there are various methods for the analysis of categorical (and mostly binary) efficacy data. The same is true here. There are different methods that are appropriate for continuous data in certain circumstances, and not every method that we discuss is appropriate for every situation. A careful assessment of the data type, the shape of the distribution (which can be examined through a relative frequency histogram or a stem-and-leaf plot), and the sample size can help justify the most appropriate analysis approach. For example, if the shape of the distribution of the random variable is symmetric or the sample size is large ( 30) the sample mean would be considered a reasonable estimate of the population mean. Parametric analysis approaches such as the two-sample t test or an analysis of variance (ANOVA) would then be appropriate. However, when the distribution is severely asymmetric, or skewed, the sample mean is a poor estimate of the population mean. In such cases a nonparametric approach would be more appropriate. [Pg.147]

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