Prior to the analysis, the number of events at baseline and final visit were normalized (to correspond to 8 hours observation time) then log-transformed via a log(1+x) transformation. 'The primary endpoint, change from baseline in signs and symptoms of GERD observed from video and cardiorespiratory monitoring, was analyzed by ANCOVA. In advisory committee meeting document for AZ’s Drug Esomeprazole, the statistical analysis for the primary endpoint was stated as: From the FDA website, I could only find one study where the log(1+x) transformation was used. However, in clinical trials, I have seen many applications of the log-transformation, but not the log(x+1) transformation. For the control group, the log(0+1) = 0, which seems to be a perfect approach in this case. Since the control group dose is considered zero and log(x) does not exist, an easy solution is to use log(x+1). Prior to the analysis, the log transformation for the dose, log(x), is usually applied. Therefore, from a dose-response analysis standpoint, the control group dose is considered as zero and the various concentrations are designed in exponential scale. The whole Effluent toxicity testing is often designed as multi-concentrations, and includes a minimum of five concentrations of effluent and one control group. For many of these biological endpoints, toxicity is manifested as a reduction in the response relative to the control group. In the Whole Effluent Toxicity testing, many different species and several endpoints are used to measure the aggregate toxic effect of an effluent. The Whole Effluent Toxicity test, one of the aquatic toxicological experiments, has been used by the US Environmental Protection Agency (USEPA) to identify effluents and receiving waters containing toxic materials, and to estimate the toxicity of waster water. The data set is from a so-called Whole Effluent Toxicity Test. The first time I had to use log(x+1) transformation is for a dose-response data set where the dose is in exponential scale with a control group dose concentration of zero. As a special case of logarithm transformation, log(x+1) or log(1+x) can also be used. We are very familiar with the typically data transformation approaches such as log transformation, square root transformation. When performing the data analysis, sometimes the data is skewed and not normal-distributed, and the data transformation is needed.

Log transformation xlstat