Claiming trend in toxicological and pharmacological dose-response studies: an overview on statistical methods and related R-Software

TL;DR

This paper reviews statistical methods for detecting trends in dose-response studies in pharmacology and toxicology, emphasizing sparse models, combined testing approaches, and their implementation in R software, while noting limitations with small sample sizes.

Contribution

It introduces a combined Tukey regression and Williams contrast test approach for trend detection, applicable to various variable types and generalizable to complex models, with software implementation.

Findings

Effective trend detection in diverse pharmacological data

Applicable to generalized linear and mixed models

Limitations with very small sample sizes

Abstract

There are very different statistical methods for demonstrating a trend in pharmacological experiments. Here, the focus is on sparse models with only one parameter to be estimated and interpreted: the increase in the regression model and the difference to control in the contrast model. This provides both p-values and confidence intervals for an appropriate effect size. A combined test consisting of the Tukey regression approach and the multiple contrast test according to Williams is recommended, which can be generalized to the generalized linear (mixed effect) model. Thus numerous variable types occurring in pharmacology/toxicology can be adequately evaluated. Software is available through CRAN packages. The most significant limitation of this approach is for designs with very small sample sizes, often in pharmacology/toxicology.