Proposal to synonymize Neoplagioporus Shimazu, 1990 with Urorchis Ozaki, 1927; taxonomic revision and the description of Urorchis takahaya n. sp.

Urorchis Ozaki, 1927 and Neoplagioporus Shimazu, 1990 (Digenea: Opecoelidae: Sphaerostomatinae) comprise species parasitic in freshwater fishes of eastern Asia, although the status of these genera is questionable. We revised these genera, primarily using evidence from a molecular phylogeny based on nuclear ribosomal DNA, including new sequences of four known and one new species. Urorchis was part of the clade of Neoplagioporus species, rendering the genus Neoplagioporus paraphyletic.

Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data.

When multiple historical controls are available, it is necessary to consider the conflicts between current and historical controls and the relationships among historical controls. One of the assumptions concerning the relationships between the parameters of interest of current and historical controls is known as the "Potential biases." Within the "Potential biases" assumption, the differences between the parameters of interest of the current control and of each historical control are defined as "potential bias parameters.

geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators.

Background: The generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias.

Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis.

The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure.