Objective The Neonatal Adverse Event Severity Scale (NAESS) was developed to improve scoring of neonatal adverse events (AEs) and accelerate neonatal drug development. This is the first validation study of the novel tool. Study design Retrospective validation study assessing the inter-rater reliability (IRR) of the NAESS. Reviewers used real-world AE data from a neonatal trial. Intra-class correlation (ICC) statistical analysis was performed. Result Sixty AEs were randomly assigned to twelve reviewers for a total of 240 severity scores. Generic and AE-specific NAESS tables were assessed. The ICC was 0.63 (95% confidence interval 0.51 to 0.73). Percent variation due to reviewer and residual error was 0.03 and 0.34, respectively. Conclusion In this first study of the NAESS tool, an ICC of 0.63 indicates moderate reliability. Results highlight the need for improved data collection on neonatal AE forms, augmented training on the NAESS tool, and will inform the prospective validation studies.

e Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species-environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

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