Infection of humans by the larval stage of the tapeworms Echinococcus granulosus sensu lato or Echinococcus multilocularis causes the life-threatening zoonoses cystic echinococcosis (CE) and alveolar echinococcosis (AE). Although cystic liver lesions are a hallmark of both diseases, course, prognosis, and patients' management decisively differ between the two. The wide and overlapping spectrum of morphologies and the limited availability of ancillary tools are challenges for pathologists to reliably diagnose and subtype echinococcosis. Here, we systematically and quantitatively recorded the pathologic spectrum in a clinically and molecularly defined echinococcosis cohort (138 specimens from 112 patients). Immunohistochemistry using a novel monoclonal antibody (mAbEmG3) was implemented, including its combined application with the mAbEm2G11. Six morphologic criteria sufficiently discriminated between CE and AE: size of smallest (CE/AE: >2/2 mm) and largest cyst (CE/AE: >25/25 mm), thickness of laminated layer (CE/AE: >0.15/0.15 mm) and pericystic fibrosis (CE/AE: >0.6/0.6 mm), striation of laminated layer (CE/AE: moderate-strong/weak), and number of cysts (CE/AE: 9/>9). Combined immunohistochemistry with mAbEm2G11 (E. multilocularis specific) and mAbEmG3 (reactive in AE and CE) was equally specific as and occasionally more sensitive than polymerase chain reaction. On the basis of these findings, we developed a diagnostic algorithm for the differential diagnosis of echinococcosis. In summary, we have not only identified the means to diagnose echinococcosis with greater certainty, but also defined morphologic criteria, which robustly discriminate between CE and AE. We expect our findings to improve echinococcosis diagnostics, especially of challenging cases, beneficially impacting the management of echinococcosis patients.
Replication studies are increasingly conducted in order to confirm original findings. However, there is no established standard how to assess replication success, and, in practice, many different approaches are used. The purpose of this paper is to refine and extend a recently proposed reverse-Bayes approach for the analysis of replication studies. We show how this method is directly related to the relative effect size, the ratio of the replication to the original effect estimate. This perspective leads to a new proposal to recalibrate the assessment of replication success, the golden level. The recalibration ensures that, for borderline significant original studies, replication success can only be achieved if the replication effect estimate is larger than the original one. Conditional power for replication success can then take any desired value if the original study is significant and the replication sample size is large enough. Compared to the standard approach to require statistical significance of both the original and replication study, replication success at the golden level offers uniform gains in project power and controls the type-I error rate if the replication sample size is not smaller than the original one. An application to data from four large replication projects shows that the new approach leads to more appropriate inferences, as it penalizes shrinkage of the replication estimate, compared to the original one, while ensuring that both effect estimates are sufficiently convincing on their own.
The reproducibility crisis has led to an increasing number of replication studies being conducted. Sample sizes for replication studies are often calculated using conditional power based on the effect estimate from the original study. However, this approach is not well suited as it ignores the uncertainty of the original result. Bayesian methods are used in clinical trials to incorporate prior information into power calculations. We propose to adapt this methodology to the replication framework and promote the use of predictive instead of conditional power in the design of replication studies. Moreover, we describe how extensions of the methodology to sequential clinical trials can be tailored to replication studies. Conditional and predictive power calculated at an interim analysis are compared and we argue that predictive power is a useful tool to decide whether to stop a replication study prematurely. A recent project on the replicability of social sciences is used to illustrate the properties of the different methods.
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.
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