Increasing efficiency of preclinical research by group sequential designs
| Autor: | Jonathan Kimmelman, John P. A. Ioannidis, Alice Schneider, Bob Siegerink, André Rex, Sophie K. Piper, Ulrike Grittner, Oscar Florez-Vargas, George Karystianis, Ian Wellwood, Ulrich Dirnagl, Konrad Neumann |
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| Přispěvatelé: | Wellwood, Ian [0000-0002-6059-9209], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Research design Drug Research and Development Biomedical Research QH301-705.5 Bayesian Method Biology Research and Analysis Methods General Biochemistry Genetics and Molecular Biology Field (computer science) 03 medical and health sciences Mathematical and Statistical Techniques 0302 clinical medicine Frequentist inference Medicine and Health Sciences Clinical Trials ddc:610 Statistical Methods Biology (General) Biomedicine Block (data storage) Pharmacology General Immunology and Microbiology business.industry Experimental Design General Neuroscience Research Assessment Probability Theory Probability Distribution Reproducibility Reliability engineering Clinical trial 030104 developmental biology Sample size determination Research Design Perspective Physical Sciences Group sequential Clinical Medicine General Agricultural and Biological Sciences business Mathematics Bayesian Statistics Statistics (Mathematics) 030217 neurology & neurosurgery |
| Zdroj: | PLoS biology 15(3), e2001307 (2017). doi:10.1371/journal.pbio.2001307 PLoS Biology, Vol 15, Iss 3, p e2001307 (2017) PLoS Biology |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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