Theory Testing and Process Evidence in Accounting Experiments

This paper discusses the role of process evidence in accounting research. We define process evidence broadly as data providing insight into how and why cause-effect relationships occur, and we provide a framework to guide the provision and evaluation of process evidence in accounting studies. Our definition allows for an expanded understanding of techniques for gathering process evidence. The framework highlights the importance of the study’s goals and theory in choosing how to provide process evidence as well as how much process evidence to provide. The paper also outlines the strengths and limitations of three approaches to providing process evidence: mediation, moderation, and multiple-study based designs. We provide recommendations for best practices for each approach to minimize threats to validity and maximize the value of process evidence.

regCOVID: Tracking publications of registered COVID-19 studies

In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications. We found 760 articles linked to 419 interventional trials (15.7% of all 2 669 COVID-19 interventional trials as of 15 August 2021), with 418 identified via abstract-link in PubMed and 342 via registry-link in ClinicalTrials.gov. Of the 419 trials publishing at least one article, 123 (29.4%) have multiple linked publications. We used an attention score to develop a prioritized list of all publications linked to COVID-19 trials and identified 58 publications that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, we found 69 linked result articles for 40 trials (13.9% of 290 total COVID-19 vaccine trials). Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link.

Identifying causal variants by fine mapping across multiple studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

Precision and Accuracy of Hyperglycemic Clamps in a Multicenter Study

Background: Application of glucose clamp methodologies in multicenter studies brings challenges for standardization. The Restoring Insulin Secretion (RISE) Consortium implemented a hyperglycemic clamp protocol across seven centers using a combination of technical and management approaches to achieve standardization. Methods: Two- stage hyperglycemic clamps with glucose targets of 200 mg/dL and >450 mg/dL were performed utilizing a centralized spreadsheet-based algorithm that guided dextrose infusion rates using bedside plasma glucose measurements. Clamp operators received initial and repeated training with ongoing feedback based on surveillance of clamp performance. The precision and accuracy of the achieved stage-specific glucose targets were evaluated, including differences by study center. We also evaluated robustness of the method to baseline physiologic differences and on-study treatment effects. Results: The RISE approach produced high overall precision (3-9% variance in achieved plasma glucose from target at various times across the procedure) and accuracy (SD <10% overall). Statistically significant but numerically small differences in achieved target glucose concentrations were observed across study centers, within the magnitude of the observed technical variability. Variation of the achieved target glucose over time in placebo-treated individuals was low (<3% variation), and the method was robust to differences in baseline physiology (youth vs adult, IGT vs diabetes status) and to differences in physiology induced by study treatments. Conclusions: The RISE approach to standardization of the hyperglycemic clamp methodology across multiple study centers produced technically excellent standardization of achieved glucose concentrations. This approach provides a reliable method for implementing glucose clamp methodology across multiple study centers.

Community completeness as a measure of restoration success: multiple-study comparisons across ecosystems and ecological groups

When restoring habitat for biodiversity, the most effective outcome will be achieved by restoration projects which target several organism groups or ecosystem types. Such integrated approaches require direct comparisons among different ecological communities while evaluating success of restoration. The Community Completeness Index (CCI) is a recently developed metric that allows such comparisons by accounting for both present and absent but otherwise suitable taxa. We empirically evaluated the applicability of CCI for assessing the outcome of ecological restoration. We analyzed how species richness and the completeness of ecological communities recover after restoration, for different ecological groups and ecosystem types, and how it develops over time after restoration. Analyses were performed on 18 datasets with per site presence-absence data from Northern Europe. Each dataset represented one of the three habitat types (mire, forest, grassland) and different ecological groups (plants, flying insects, epigeic invertebrates). Datasets contained pristine, degraded and restored sites. We calculated the dark diversity and subsequently CCI based on species co-occurrences. Our multiple-study analyses revealed that CCI of grassland plant communities increased faster after restoration than invertebrate communities or plant communities in forests and mires. In addition, flying insect communities demonstrated significantly highest CCI in pristine mires. Some results were significant only for richness but not for CCI indicating species pool effect. Finally, completeness and species richness of restored communities increased with time since restoration. As such, our study demonstrated that CCI is a useful tool in evaluating restoration success across different organism groups and ecosystem types.

Identifying Causal Variants by Fine Mapping Across Multiple Studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.

Insectivory characteristics of the Japanese marten (Martes melampus): a qualitative review

Insects are rich in protein and thus are important substitute foods for many species of generalist feeders. This study reviews insectivory characteristics of the Japanese marten (Martes melampus) based on current literature. Across the 16 locations (14 studies) in the Japanese archipelago, a total of 80 different insects (including those only identified at genus, family, or order level) were listed as marten food, 26 of which were identified at the species level. The consumed insects were categorised by their locomotion types, and the Japanese martens exploited not only grounddwelling species, but also arboreal, flying, and underground-dwelling insects, taking advantage of their arboreality and ability of agile pursuit predation. Notably, immobile insects such as egg mass of Mantodea spp, as well as pupa/larvae of Vespula flaviceps and Polistes spp. from wasp nests were consumed by the Japanese marten in multiple study areas. This review shows dietary generalism (specifically ‘food exploitation generalism’) of the Japanese marten in terms of non-nutritive properties (i.e., locomotion ability of prey).