Increasing efficiency of preclinical research by group sequential designs
Konrad Neumann; Ulrike Grittner; Sophie K. Piper; André Rex; Oscar Flórez-Vargas; George Karystianis; Alice Schneider; Ian Wellwood · 2017 · PLoS Biology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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 t
Abstract by Konrad Neumann; Ulrike Grittner; Sophie K. Piper; André Rex; Oscar Flórez-Vargas; George Karystianis; Alice Schneider; Ian Wellwood, PLoS Biology (2017) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results*
Negative / Null Result ReportThe harmonic mean p -value for combining dependent tests
Negative / Null Result ReportGeneralizability of heterogeneous treatment effect estimates across samples
Negative / Null Result ReportNumerical predictors of arithmetic success in grades 1–6
Negative / Null Result ReportMethods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics
Negative / Null Result ReportShould multiple imputation be the method of choice for handling missing data in randomized trials?
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1371/journal.pbio.2001307