Effects of beach remourishment on the benthic macrofauna and the fishes of the nearshore zone at Sebastian Inlet State Recreation Area

Approximately 100,000 cubic yards of sand was transported to the ocean beach to renourish the eroded beach front during the period December 1985 through May 1986. The ocean beach at Sebastian Inlet SRA was previously studied in a project examining the benthic macrofauna and the fishes of the nearshore zone during 1981-1982 (Allenbaugh, 1984; Peters, 1984; Nelson, unpublished). In view of the existing data, the US Army Corps of Engineers provided funding to study the effects of the beach renourishment activities at Sebastian Inlet SRA on the benthic macrofauna and the fishes of the nearshore zone. This is the report on the results of this study

Evaluating methodological quality of Prognostic models Including Patient-reported HeAlth outcomes in oncologY (EPIPHANY): A systematic review protocol

Introduction While there is mounting evidence of the independent prognostic value of patient-reported outcomes (PROs) for overall survival (OS) in patients with cancer, it is known that the conduct of these studies may hold a number of methodological challenges. The aim of this systematic review is to evaluate the quality of published studies in this research area, in order to identify methodological and statistical issues deserving special attention and to also possibly provide evidence-based recommendations. Methods and analysis An electronic search strategy will be performed in PubMed to identify studies developing or validating a prognostic model which includes PROs as predictors. Two reviewers will independently be involved in data collection using a predefined and standardised data extraction form including information related to study characteristics, PROs measures used and multivariable prognostic models. Studies selection will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, with data extraction form using fields from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist for multivariable models. Methodological quality assessment will also be performed and will be based on prespecified domains of the CHARMS checklist. As a substantial heterogeneity of included studies is expected, a narrative evidence synthesis will also be provided. Ethics and dissemination Given that this systematic review will use only published data, ethical permissions will not be required. Findings from this review will be published in peer-reviewed scientific journals and presented at major international conferences. We anticipate that this review will contribute to identify key areas of improvement for conducting and reporting prognostic factor analyses with PROs in oncology and will lay the groundwork for developing future evidence-based recommendations in this area of research. Prospero registration number CRD42018099160

A robust imputation method for missing responses and covariates in sample selection models

Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputation techniques, especially in the absence of exclusion restrictions and deviation from normality. Motivated by the 2003-2004 NHANES data, where previous authors have studied the effect of socio-economic status on blood pressure with missing data on income variable, we proposed the use of a robust imputation technique based on the selection-t sample selection model. The imputation method, which is developed within the frequentist framework, is compared with competing alternatives in a simulation study. The results indicate that the robust alternative is not susceptible to the absence of exclusion restriction- a property inherited from the parent selection-t model- and performs better than models based on the normal assumption even when the data is generated from the normal distribution. Applications to missing outcome and covariate data further corroborate the robustness properties of the pro-posed method. We implemented the proposed approach within the MICE environment in R Statistical Software

Distribution Patterns of Diatoms in Cedar Run

Author Institution: Department of Botany, The Ohio State UniversityThe diatom communities of Cedar Run, Champaign County, Ohio were studied to determine their spatial and seasonal variation. Collections were obtained using glass slides as an artificial substratum and the diatom community was determined by identifying and counting approximately 1,000 valves on each slide. There were 190 diatom taxa identified, 82 of which were new records for Ohio. A multivariate analysis of the data resulted in unique clustering of points for each station, indicating a heterogeneous diatom assemblage in Cedar Run. Results were interpreted as indicating that areal effect on diatom community patterns masked that of seasonal influence in Cedar Run. A seasonal pattern was apparent when data for each station were considered separately from other stations