Clinical translation from bench to bedside often remains challenging even despite promising preclinical evidence. Among many drivers like biological complexity or poorly understood disease pathology, preclinical evidence often lacks desired robustness. Reasons include low sample sizes, selective reporting, publication bias, and consequently inflated effect sizes. In this context, there is growing consensus that confirmatory multicenter studies -by weeding out false positives- represent an important step in strengthening and generating preclinical evidence before moving on to clinical research. However, there is little guidance on what such a preclinical confirmatory study entails and when it should be conducted in the research trajectory. To close this gap, we organized a workshop to bring together statisticians, clinicians, preclinical scientists, and meta-researcher to discuss and develop recommendations that are solution-oriented and feasible for practitioners. Herein, we summarize and review current approaches and outline strategies that provide decision-critical guidance on when to start and subsequently how to plan a confirmatory study. We define a set of minimum criteria and strategies to strengthen validity before engaging in a confirmatory preclinical trial, including sample size considerations that take the inherent uncertainty of initial (exploratory) studies into account. Beyond this specific guidance, we highlight knowledge gaps that require further research and discuss the role of confirmatory studies in translational biomedical research. In conclusion, this workshop report highlights the need for close interaction and open and honest debate between statisticians, preclinical scientists, meta-researchers (that conduct research on research), and clinicians already at an early stage of a given preclinical research trajectory.

Statistical significance of both the original and the replication study is a commonly used criterion to assess replication attempts, also known as the two-trials rule in drug development. However, replication studies are sometimes conducted although the original study is non-significant, in which case Type-I error rate control across both studies is no longer guaranteed. We propose an alternative method to assess replicability using the sum of p -values from the two studies. The approach provides a combined p -value and can be calibrated to control the overall Type-I error rate at the same level as the two-trials rule but allows for replication success even if the original study is non-significant. The unweighted version requires a less restrictive level of significance at replication if the original study is already convincing which facilitates sample size reductions of up to 10%. Downweighting the original study accounts for possible bias and requires a more stringent significance level and larger sample sizes at replication. Data from four large-scale replication projects are used to illustrate and compare the proposed method with the two-trials rule, meta-analysis and Fisher’s combination method.

In several large-scale replication projects, statistically non-significant results in both the original and the replication study have been interpreted as a ‘replication success.’ Here, we discuss the logical problems with this approach: Non-significance in both studies does not ensure that the studies provide evidence for the absence of an effect and ‘replication success’ can virtually always be achieved if the sample sizes are small enough. In addition, the relevant error rates are not controlled. We show how methods, such as equivalence testing and Bayes factors, can be used to adequately quantify the evidence for the absence of an effect and how they can be applied in the replication setting. Using data from the Reproducibility Project: Cancer Biology, the Experimental Philosophy Replicability Project, and the Reproducibility Project: Psychology we illustrate that many original and replication studies with ‘null results’ are in fact inconclusive. We conclude that it is important to also replicate studies with statistically non-significant results, but that they should be designed, analyzed, and interpreted appropriately.

Replication studies are increasingly conducted to assess the credibility of scientific findings. Most of these replication attempts target studies with a superiority design, but there is a lack of methodology regarding the analysis of replication studies with alternative types of designs, such as equivalence. In order to fill this gap, we propose two approaches, the two‐trials rule and the sceptical two one‐sided tests (TOST) procedure, adapted from methods used in superiority settings. Both methods have the same overall Type‐I error rate, but the sceptical TOST procedure allows replication success even for nonsignificant original or replication studies. This leads to a larger project power and other differences in relevant operating characteristics. Both methods can be used for sample size calculation of the replication study, based on the results from the original one. The two methods are applied to data from the Reproducibility Project: Cancer Biology.

Replication studies are increasingly conducted to assess the credibility of scientific findings. Most of these replication attempts target studies with a superiority design, but there is a lack of methodology regarding the analysis of replication studies with alternative types of designs, such as equivalence. In order to fill this gap, we propose two approaches, the two-trials rule and the sceptical TOST procedure, adapted from methods used in superiority settings. Both methods have the same overall Type-I error rate, but the sceptical TOST procedure allows replication success even for non-significant original or replication studies. This leads to a larger project power and other differences in relevant operating characteristics. Both methods can be used for sample size calculation of the replication study, based on the results from the original one. The two methods are applied to data from the Reproducibility Project: Cancer Biology.