Moreover, Bayesian analysis enables the researcher to use prestudy information as a basis for the prior information about the measure of interest. Traditional frequentist statistics can be used to split the overall probability of type I error (α–error) to account for multiple testing, but Bayesian methods are particularly suited, as they can incorporate information from earlier stages of the study. However, interim analyses come at a statistical cost, and special analysis methods and careful preplanning are required. In contrast, a group sequential design is a type of adaptive design that allows for early stopping of an experiment because of efficacy or futility, based on interim analyses before all experimental units are spent, thereby offering an increase in efficiency. Moreover, 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.Ĭonventional study designs in experimental preclinical biomedicine use nonsequential approaches, in which group sizes are predetermined and fixed, and the decision to either accept the (alternative) hypothesis or fail to reject the null hypothesis is made after spending all experimental units in each group. Here, we propose the use of sequential study designs to reduce the number of experimental animals required, as well as to increase the efficiency of current preclinical biomedical research. Yet, such calls also potentially antagonize the goal of minimizing burdens on animals. Therefore, various research bodies (e.g., National Institutes of Health, United Kingdom Academy of Medical Sciences) have called for increased sample sizes, as well as other design improvements in preclinical research. Such results lack reproducibility, and the effect sizes are often substantially overestimated (“Winner’s curse”). Consequently, true positives are often missed (false negatives), and many statistically significant findings are due to chance (false positives). Typical sample sizes are small, around n = 8 per group ( ), and are only sufficient to detect relatively large sizes of effects. Group sizes in preclinical research are seldom informed by statistical power considerations but rather are chosen on practicability. 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.
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. 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. 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. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. 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. Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs.
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