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Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. The needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

This article introduces the 'staircase' design, derived from the zigzag pattern of steps along the diagonal of a stepped wedge design schematic where clusters switch from control to intervention conditions. Unlike a complete stepped wedge design where all participating clusters must collect and provide data for the entire trial duration, clusters in a staircase design are only required to be involved and collect data for a limited number of pre- and post-switch periods. This could alleviate some of the burden on participating clusters, encouraging involvement in the trial and reducing the likelihood of attrition. Staircase designs are already being implemented, although in the absence of a dedicated methodology, approaches to sample size and power calculations have been inconsistent. We provide expressions for the variance of the treatment effect estimator when a linear mixed model for an outcome is assumed for the analysis of staircase designs in order to enable appropriate sample size and power calculations. These include explicit variance expressions for basic staircase designs with one pre- and one post-switch measurement period. We show how the variance of the treatment effect estimator is related to key design parameters and demonstrate power calculations for examples based on a real trial. [ABSTRACT FROM AUTHOR]

There are sometimes cost, scientific, or logistical reasons to allocate individuals unequally in an individually randomized trial. In cluster randomized trials we can allocate clusters unequally and/or allow different cluster size between trial arms. We consider parallel group designs with a continuous outcome, and optimal designs that require the smallest number of individuals to be measured given the number of clusters. Previous authors have derived the optimal allocation ratio for clusters under different variance and/or intracluster correlations (ICCs) between arms, allowing different but prespecified cluster sizes by arm. We derive closed‐form expressions to identify the optimal proportions of clusters and of individuals measured for each arm, thereby defining optimal cluster sizes, when cluster size can be chosen freely. When ICCs differ between arms but the variance is equal, the optimal design allocates more than half the clusters to the arm with the higher ICC, but (typically only slightly) less than half the individuals and hence a smaller cluster size. We also describe optimal design under constraints on the number of clusters or cluster size in one or both arms. This methodology allows trialists to consider a range for the number of clusters in the trial and for each to identify the optimal design. Except if there is clear prior evidence for the ICC and variance by arm, a range of values will need to be considered. Researchers should choose a design with adequate power across the range, while also keeping enough clusters in each arm to permit the intended analysis method. [ABSTRACT FROM AUTHOR]

Aim: Cohort data suggest that anastomotic leak occurs after 8% of right colectomies causing significant morbidity and mortality. Patient selection, intra‐operative factors, and technical variation all contribute to risk of leak. The EAGLE study will assess whether implementation of the European Society of Coloproctology (ESCP) Safe Anastomosis Intervention reduces anastomotic leak following right colectomy. Methods: An international, multi‐centre, cluster randomised trial will be undertaken with hospitals as clusters. Hospitals will be recruited in a number of distinct phases, with each phase following the same research plan, in which clusters are randomised to one of three, staggered (dog‐leg) schedules for implementation of the Safe Anastomosis Intervention. Results: Results from different phases will be meta‐analysed. The intervention is a three‐component behavioural change programme for surgeons, anaesthetists and operating room staff, supported by an online learning environment. All colorectal surgical units around the world will be eligible. Adults undergoing elective or emergency right colectomy or ileocaecal resection, by any approach and for any indication will be included. The primary outcome is 30‐day anastomotic leak rate, defined as clinical or radiologically‐detected leak or intra‐abdominal or pelvic collection. Assuming hospitals provide data for an average of 10 patients per two month recruitment period, 333 clusters (4440 patients in total) will allow for detection of an absolute risk reduction of anastomotic leak from 8.1% to 5.6% (relative risk reduction 30%). This protocol adheres to Standard Protocol Items: Recommendations for Intervention Trials (SPIRIT). Discussion: The protocol describes the methods for an evaluation of a hospital‐level, education‐based quality improvement intervention targeted to reduce the life‐threatening surgical complication of anastomotic leak. [ABSTRACT FROM AUTHOR]