Good statistical practice in pharmacology Problem 1

Abstract

This paper is intended to assist pharmacologists in making the most of statistical analysis and in avoiding common errors that can lead to false conclusions. A scenario is presented where a pathway inhibitor increased blood pressure responses to an agonist by about one third. The fictional experimenter concludes that the inhibitor enhanced the responses to the agonist, but has not applied any statistical analysis. Questions are asked of the reader, and a discussion of the author's answers is presented. The agonist responses have unequal standard errors, as often seen in data like these concentration-response curves with responses expressed as change from baseline. The uneven variability (heteroscedasticity) violates an assumption of conventional parametric statistical analyses, but can be corrected by data transformation. Expressing the data as absolute blood pressure and then transforming it to log blood pressure eliminated the heteroscedasticity, but made evident an effect of the inhibitor on baseline blood pressure. Statistical analysis is a sensible precaution against mistakes, but cannot protect against all erroneous conclusions. In this scenario, the inhibitor reduced the blood pressure and increased responses to the agonist. However, it is likely that the latter effect was a consequence of the former and thus no conclusion can be safely drawn about any direct interaction between the agonist and the pathway inhibitor from this experiment. Where results are awkward to interpret because of confounding factors such as an altered baseline, statistical analysis may not be very useful in supporting a safe conclusion.