Simkus, A. and Coolen, F. P. A. and Coolen-Maturi, T. and Karp, N. A. and Bendtsen, C. (2022) 'Statistical reproducibility for pairwise t-tests in pharmaceutical research.', Statistical Methods in Medical Research, 31 (4). pp. 673-688.
This paper investigates statistical reproducibility of the t-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference (NPI) method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an NPI algorithm to calculate the reproducibility of the t-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply NPI reproducibility to a real life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s d and the p-value. We also compare the reproducibility of the t-test with the reproducibility of the Wilcoxon Mann-Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.
|Full text:||(AM) Accepted Manuscript|
Download PDF (751Kb)
|Full text:||(VoR) Version of Record|
Available under License - Creative Commons Attribution 4.0.
Download PDF (OnlineFirst) (3411Kb)
|Publisher Web site:||https://doi.org/10.1177/09622802211041765|
|Publisher statement:||This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).|
|Date accepted:||31 July 2021|
|Date deposited:||02 August 2021|
|Date of first online publication:||02 December 2021|
|Date first made open access:||02 August 2021|
Save or Share this output
|Look up in GoogleScholar|