Statistical Planning of Translational Research

• Applicants: Professor Dr. Tim Friede; Professor Dr. Frank Konietschke
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 455924146

This project will be a joint venture of the Department of Medical Statistics of the University Medical Center Göttingen and the Institute of Biometry and Clinical Epidemiology of the Charité Universitätsmedizin Berlin. It aspires to improve the statistical aspects in the translation of basic research to clinical sciences to meet standards set by the 3R’s principle of animal experiments: Replacement, Reduction, Refinement and by animal welfare authorities. Any trial should start with its careful planning and especially with sample size calculations, in particular with regard to sample size and power considerations. The planning phase of an experiment is key, since errors in the statistical planning can have severe consequences on both the results and conclusions drawn from the data. In translational research (preclinical and early clinical), false conclusions highly affect subsequent trials and thus, mistakes proliferate, a rather unethical outcome. In statistical practice, most studies are planned based on t-tests and Wald-type statistics (including ANOVA) and make some strict distributional assumptions. Sample sizes are typically small and if planning assumptions are not met, the trials are either underpowered or too large and thus result in wrong conclusions and waste resources and might even be misleading. On the other hand, nonparametric ranking methods (such as Wilcoxon-Mann-Whitney test, Brunner-Munzel test, multiple contrast tests and their generalizations) are excellent alternatives to such parametric approaches. However, sample size formulas as well as detailed power analyses are not implemented yet for broad classes of such tests. Furthermore, group sequential designs and adaptive designs including sample size re-estimation provide a flexible research framework and are therefore desirable in all phases of translational research. Early stopping of trials is not only worthwhile from cost saving perspectives, animal welfare rises it to a different level. In this project, approximate solutions using innovative resampling algorithms will be developed, since sample sizes are typically small and often limited. Beyond developing nonparametric group sequential tests, we will explore the application of existing methods in small sample settings and their implementation in the general work flow of (confirmatory) animal trials overall. All in all, this project will not only develop new statistical procedures and algorithms, but will also improve work flows and ultimately the ethical standards of preclinical studies.

• DFG Programme: Research Grants