Reducing Costs and Improving Fit for Clinical Trials that Have Positive-Valued Data.

In many fields of research variables are often both continuous and restricted to be positive. We analyzed 70 endpoints that contained continuous and positive dependent variables from 6 clinical and 3 preclinical trials. The impact of data transformation and adjustment for baseline on the fit of the model was studied. On average, including baseline as a covariate decreases the necessary sample size to achieve a particular precision in the treatment effect estimate by about 70% as compared to a model that ignores baseline, or by 20% to 33% as compared to models that only adjust for baseline in the response. Additionally, log transformation of the endpoint (either the direct outcome or a baseline-adjusted outcome) appears to decrease the sample size needed on average by 20% to 35%. We draw three conclusions from this work. First, if a baseline is available, use of baseline as a covariate should always be undertaken. Second, although we recommend exploration of data from previous studies, percent change from baseline analyses should not be undertaken unless there is strong empirical evidence that for that endpoint it is preferred. Finally, and again with the caveat that nothing replaces exploration of data from previous studies, log transformation ought to be the default analysis of positive data unless exploration of previous data provides convincing evidence that the natural scale is preferred. [ABSTRACT FROM AUTHOR]