Usually, AUC data mentioned above (Area Under Curve, which is based on blood levels) and antibody titer values are log-normal distributed(source data, noted as X), which means, if you take a log-transformation against X, namely Y=log(X), then Y follows the normal distribution (then Y has good properties to perform further statistical analysis). Both GMT and GMTR are wildly presented in statistical analysis reports. In vaccine trials (I worked before) where the interested values are antibody titers, the geometric mean is also called Geometric Mean Titer (GMT), while geometric mean ratio referred as Geometric Mean Titer Ratio (GMTR, also named “n-fold rise”). You can prove it mathematically by playing some log-transformations: 315⁄ 83.2077 (the ratio of two geometric means) simply gets 0.941199 (geometric mean ratio), which can be also derived by calculating the geometric mean of a ratio (see above). Similarly,ĬALCULATED gmTest / CALCULATED gmRef as gmrħ8. It can also be calculated manually byĪctually this kind of geometric mean (of a ratio) is more often called geometric mean ratio(a ratio of two geometric means, in this case, geometric mean of TestAUC and geometric mean of RefAUC). In a example, TTEST procedure reports a geometric mean as 0.9412, which is the geometric mean of a ratio, TestAUC/RefAUC. Just read since SAS 9.2, the TTEST procedure also natively supports Equivalence Test by simply adding a TOST option (Two one-sided tests). _ Programmers Need to Learn Statistics Or I will Kill Them All –Zed A.
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