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| Title: | Attach importance of the bootstrap t test against student's t test in clinical epidemiology : a demonstrative comparison using COVID-19 as an example | Authors: | Zhao, S Yang, ZY Musa, SS Ran, JJ Chong, MKC Javanbakht, M He, DH Wang, MH |
Issue Date: | 2021 | Source: | Epidemiology and infection, 2021, v. 149, e107 | Abstract: | Student's t test is valid for statistical inference under the normality assumption or asymptotically. By contrast, although the bootstrap t test was proposed in 1993, it is seldom adopted in medical research. We aim to demonstrate that the bootstrap t test outperforms Student's t test under normality in data. Using random data samples from normal distributions, we evaluated the testing performance, in terms of true-positive rate (TPR) and false-positive rate and diagnostic abilities, in terms of the area under the curve (AUC), of the bootstrap t test and Student's t test. We explore the AUC of both tests with varying sample size and coefficient of variation. We compare the testing outcomes using the COVID-19 serial interval (SI) data in Shenzhen and Hong Kong, China, for demonstration. With fixed TPR, the bootstrap t test maintained the equivalent accuracy in TPR, but significantly improved the true-negative rate from the Student's t test. With varying TPR, the diagnostic ability of bootstrap t test outperformed or equivalently performed as Student's t test in terms of the AUC. The equivalent performances are possible but rarely occur in practice. We find that the bootstrap t test outperforms by successfully detecting the difference in COVID-19 SI, which is defined as the time interval between consecutive transmission generations, due to sex and non-pharmaceutical interventions against the Student's t test. We demonstrated that the bootstrap t test outperforms Student's t test, and it is recommended to replace Student's t test in medical data analysis regardless of sample size. | Keywords: | Bootstrap t test Clinical epidemiology COVID-19 Serial interval Statistical hypothesis testing |
Publisher: | Cambridge University Press | Journal: | Epidemiology and infection | ISSN: | 0950-2688 | EISSN: | 1469-4409 | DOI: | 10.1017/S0950268821001047 | Rights: | © The Author(s), 2021. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons AttributionNonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use. The following publication Zhao S, Yang Z, Musa SS, Ran J, Chong MKC, Javanbakht M, He D, Wang MH (2021). Attach importance of the bootstrap t test against Student’s t test in clinical epidemiology: a demonstrative comparison using COVID-19 as an example. Epidemiology and Infection 149, e107, 1–6 is available at https://doi.org/10.1017/S0950268821001047 |
| Appears in Collections: | Journal/Magazine Article |
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