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Increasing efficiency of preclinical research by group sequential designs
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CC BY
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Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain.

Subject:
Biology
Life Science
Material Type:
Reading
Provider:
PLOS Biology
Author:
Alice Schneider
Andre Rex
Bob Siegerink
George Karystianis
Ian Wellwood
John P. A. Ioannidis
Jonathan Kimmelman
Konrad Neumann
Oscar Florez-Vargas
Sophie K. Piper
Ulrich Dirnagl
Ulrike Grittner
Date Added:
08/07/2020
P values in display items are ubiquitous and almost invariably significant: A survey of top science journals
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CC BY
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P values represent a widely used, but pervasively misunderstood and fiercely contested method of scientific inference. Display items, such as figures and tables, often containing the main results, are an important source of P values. We conducted a survey comparing the overall use of P values and the occurrence of significant P values in display items of a sample of articles in the three top multidisciplinary journals (Nature, Science, PNAS) in 2017 and, respectively, in 1997. We also examined the reporting of multiplicity corrections and its potential influence on the proportion of statistically significant P values. Our findings demonstrated substantial and growing reliance on P values in display items, with increases of 2.5 to 14.5 times in 2017 compared to 1997. The overwhelming majority of P values (94%, 95% confidence interval [CI] 92% to 96%) were statistically significant. Methods to adjust for multiplicity were almost non-existent in 1997, but reported in many articles relying on P values in 2017 (Nature 68%, Science 48%, PNAS 38%). In their absence, almost all reported P values were statistically significant (98%, 95% CI 96% to 99%). Conversely, when any multiplicity corrections were described, 88% (95% CI 82% to 93%) of reported P values were statistically significant. Use of Bayesian methods was scant (2.5%) and rarely (0.7%) articles relied exclusively on Bayesian statistics. Overall, wider appreciation of the need for multiplicity corrections is a welcome evolution, but the rapid growth of reliance on P values and implausibly high rates of reported statistical significance are worrisome.

Subject:
Mathematics
Statistics and Probability
Material Type:
Reading
Provider:
PLOS ONE
Author:
Ioana Alina Cristea
John P. A. Ioannidis
Date Added:
08/07/2020
Statistics for Applications
Conditional Remix & Share Permitted
CC BY-NC-SA
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This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Kempthorne, Peter
Date Added:
02/01/2015
Statistics for Applications
Conditional Remix & Share Permitted
CC BY-NC-SA
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This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Rigollet, Philippe
Date Added:
09/01/2016
Think Bayes: Bayesian Statistics Made Simple
Conditional Remix & Share Permitted
CC BY-NC
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The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods.

Subject:
Applied Science
Computer Science
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
Green Tea Press
Author:
Allen Downey
Date Added:
01/01/2012