BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data

This article introduces the R package BayesCTDesign for two-arm randomized Bayesian trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The package BayesCTDesign, which is available from the Comprehensive R Archive Network, has two simulation functions, historic_sim() and simple_sim() for studying trial characteristics under user-defined scenarios, and two methods print() and plot() for displaying summaries of the simulated trial characteristics. The package BayesCTDesign works with two-arm trials with equal sample sizes per arm. The package BayesCTDesign allows a user to study Gaussian, Poisson, Bernoulli, Weibull, lognormal, and piecewise exponential outcomes. Power for two-sided hypothesis tests at a user-defined α is estimated via simulation using a test within each simulation replication that involves comparing a 95% credible interval for the outcome specific treatment effect measure to the null case value. If the 95% credible interval excludes the null case value, then the null hypothesis is rejected, else the null hypothesis is accepted. In the article, the idea of including historical control data in a Bayesian analysis is reviewed, the estimation process of BayesCTDesign is explained, and the user interface is described. Finally, the BayesCTDesign is illustrated via several examples

Predictive modeling for perinatal mortality in resource-limited settings

Importance: The overwhelming majority of fetal and neonatal deaths occur in low- and middle-income countries. Fetal and neonatal risk assessment tools may be useful to predict the risk of death.Objective: To develop risk prediction models for intrapartum stillbirth and neonatal death.Design, setting, and participants: This cohort study used data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women\u27s and Children\u27s Health Research population-based vital registry, including clinical sites in South Asia (India and Pakistan), Africa (Democratic Republic of Congo, Zambia, and Kenya), and Latin America (Guatemala). A total of 502 648 pregnancies were prospectively enrolled in the registry.Exposures: Risk factors were added sequentially into the data set in 4 scenarios: (1) prenatal, (2) predelivery, (3) delivery and day 1, and (4) postdelivery through day 2.Main outcomes and measures: Data sets were randomly divided into 10 groups of 3 analysis data sets including training (60%), test (20%), and validation (20%). Conventional and advanced machine learning modeling techniques were applied to assess predictive abilities using area under the curve (AUC) for intrapartum stillbirth and neonatal mortality.Results: All prenatal and predelivery models had predictive accuracy for both intrapartum stillbirth and neonatal mortality with AUC values 0.71 or less. Five of 6 models for neonatal mortality based on delivery/day 1 and postdelivery/day 2 had increased predictive accuracy with AUC values greater than 0.80. Birth weight was the most important predictor for neonatal death in both postdelivery scenarios with independent predictive ability with AUC values of 0.78 and 0.76, respectively. The addition of 4 other top predictors increased AUC to 0.83 and 0.87 for the postdelivery scenarios, respectively.Conclusions and relevance: Models based on prenatal or predelivery data had predictive accuracy for intrapartum stillbirths and neonatal mortality of AUC values 0.71 or less. Models that incorporated delivery data had good predictive accuracy for risk of neonatal mortality. Birth weight was the most important predictor for neonatal mortality

The Lantern Vol. 34, No. 2, May 1968

• The Man Without a System • A Medal for Malcolm • On Hearing That Tonya Will Be Married • The Black Sea • Odyssey \u2767 • Second Poem to Chris • Singularity • Period 5-A Began • Long and Aching Ride • Souvenirs • My Eschatological Epitaph • Discotheque • Some Borrowed Words • False Breakthrough • Shore Morning • The Beholder • Thursday Childless • A Most Prominent Role • It Ran Out • Shades of the Living • The Dark Night of the Mind II • One Step Beyond the Doors • A Note of Thanks to My Parents and Teachers • To a Dead Hippie • A Scrap • Love • Haiku No. 30 • Rachel • There Is No Present • Winter Woods • One Hundred Per Cent Genuine • Heaven • Silence Is Like God • I Soaked Up Silence • Opened Letter From Whistler Homer, Insaned Assailant • Sol Clutch Rides Tonight • I Have Seen Destruction • Upon That Night • That\u27s Weird • Alone • Kathy\u27s Tune • On Walking Home • The Wheel • Some Excuse, at Least • Freedom to Flap • Awareness • Okay, You Guys • You Say You Dream • Bacci Miahttps://digitalcommons.ursinus.edu/lantern/1093/thumbnail.jp

The Use of Flow-Injection Analysis with Chemiluminescence Detection of Aqueous Ferrous Iron in Waters Containing High Concentrations of Organic Compounds

An evaluation of flow-injection analysis with chemiluminescence detection (FIA-CL) to quantify Fe2+(aq) in freshwaters was performed. Iron-coordinating and/or iron-reducing compounds, dissolved organic matter (DOM), and samples from two natural water systems were used to amend standard solutions of Fe2+(aq). Slopes of the response curves from ferrous iron standards (1 – 100 nM) were compared to the response curves of iron standards containing the amendments. Results suggest that FIA-CL is not suitable for systems containing ascorbate, hydroxylamine, cysteine or DOM. Little or no change in sensitivity occurred in solutions of oxalate and glycine or in natural waters with little organic matter

Predictive Modeling for Perinatal Mortality in Resource-Limited Settings.

Importance: The overwhelming majority of fetal and neonatal deaths occur in low- and middle-income countries. Fetal and neonatal risk assessment tools may be useful to predict the risk of death. Objective: To develop risk prediction models for intrapartum stillbirth and neonatal death. Design, Setting, and Participants: This cohort study used data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women\u27s and Children\u27s Health Research population-based vital registry, including clinical sites in South Asia (India and Pakistan), Africa (Democratic Republic of Congo, Zambia, and Kenya), and Latin America (Guatemala). A total of 502 648 pregnancies were prospectively enrolled in the registry. Exposures: Risk factors were added sequentially into the data set in 4 scenarios: (1) prenatal, (2) predelivery, (3) delivery and day 1, and (4) postdelivery through day 2. Main Outcomes and Measures: Data sets were randomly divided into 10 groups of 3 analysis data sets including training (60%), test (20%), and validation (20%). Conventional and advanced machine learning modeling techniques were applied to assess predictive abilities using area under the curve (AUC) for intrapartum stillbirth and neonatal mortality. Results: All prenatal and predelivery models had predictive accuracy for both intrapartum stillbirth and neonatal mortality with AUC values 0.71 or less. Five of 6 models for neonatal mortality based on delivery/day 1 and postdelivery/day 2 had increased predictive accuracy with AUC values greater than 0.80. Birth weight was the most important predictor for neonatal death in both postdelivery scenarios with independent predictive ability with AUC values of 0.78 and 0.76, respectively. The addition of 4 other top predictors increased AUC to 0.83 and 0.87 for the postdelivery scenarios, respectively. Conclusions and Relevance: Models based on prenatal or predelivery data had predictive accuracy for intrapartum stillbirths and neonatal mortality of AUC values 0.71 or less. Models that incorporated delivery data had good predictive accuracy for risk of neonatal mortality. Birth weight was the most important predictor for neonatal mortality

Movements of marine fish and decapod crustaceans: Process, theory and application

Many marine species have a multi-phase ontogeny, with each phase usually associated with a spatially and temporally discrete set of movements. For many fish and decapod crustaceans that live inshore, a tri-phasic life cycle is widespread, involving: (1) the movement of planktonic eggs and larvae to nursery areas; (2) a range of routine shelter and foraging movements that maintain a home range; and (3) spawning migrations away from the home range to close the life cycle. Additional complexity is found in migrations that are not for the purpose of spawning and movements that result in a relocation of the home range of an individual that cannot be defined as an ontogenetic shift. Tracking and tagging studies confirm that life cycle movements occur across a wide range of spatial and temporal scales. This dynamic multi-scale complexity presents a significant problem in selecting appropriate scales for studying highly mobile marine animals. We address this problem by first comprehensively reviewing the movement patterns of fish and decapod crustaceans that use inshore areas and present a synthesis of life cycle strategies, together with five categories of movement. We then examine the scale-related limitations of traditional approaches to studies of animal-environment relationships. We demonstrate that studies of marine animals have rarely been undertaken at scales appropriate to the way animals use their environment and argue that future studies must incorporate animal movement into the design of sampling strategies. A major limitation of many studies is that they have focused on: (1) a single scale for animals that respond to their environment at multiple scales or (2) a single habitat type for animals that use multiple habitat types. We develop a hierarchical conceptual framework that deals with the problem of scale and environmental heterogeneity and we offer a new definition of 'habitat' from an organism-based perspective. To demonstrate that the conceptual framework can be applied, we explore the range of tools that are currently available for both measuring animal movement patterns and for mapping and quantifying marine environments at multiple scales. The application of a hierarchical approach, together with the coordinated integration of spatial technologies offers an unprecedented opportunity for researchers to tackle a range of animal-environment questions for highly mobile marine animals. Without scale-explicit information on animal movements many marine conservation and resource management strategies are less likely to achieve their primary objectives

Building a Predictive Model of Low Birth Weight in Low- and Middle-Income Countries: A Prospective Cohort Study

BACKGROUND: Low birth weight (LBW, \u3c 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants. METHODS: We developed predictive models for LBW using the NICHD Global Network for Women\u27s and Children\u27s Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 - December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine. RESULTS: We report a rate of LBW of 13.8% among the eight Global Network sites from 2017-2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW. CONCLUSIONS: Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk