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

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

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

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

Weighted Walking Influences Lower Extremity Coordination in Children on the Autism Spectrum

© The Author(s) 2018. There is sparse quantitative research regarding gait coordination patterns of children on the autism spectrum, though previous studies, relying only upon observational data, have alluded to characteristically poor movement coordination. This study compared walking with a weighted vest, a backpack carriage, and an unloaded walking condition on lower extremity coordination among 10 male children (aged 8–17 years) on the autism spectrum. All participants completed 15 gait trials in the following three conditions: (a) unloaded, (b) wearing a backpack weighted with 15% body mass, and (c) wearing a vest weighted with 15% body mass. We used continuous relative phase analysis to quantify lower extremity coordination and analyzed data through both group and single-subject comparisons. We used the Model Statistic to test for statistical significance at each of the normalized data points for each segment couple (thigh–leg, leg–foot, and thigh–foot). The first 10 and last 10 stride blocks were tested for possible accommodation strategies. Group comparisons revealed no coordination changes among the three conditions (likely due to insufficient statistical power), while single-subject comparisons exposed significant decreased variability in gait coordination patterns (p \u3c.05) in both loaded conditions, relative to the unloaded condition. These participants exhibited variable coordination patterns during the unloaded gait. When walking with loads, coordination pattern variability of the lower extremities was decreased. This finding suggests that walking while carrying or wearing heavy objects may reduce the number of potential motor pattern choices and thus decrease the overall variability of lower extremity movement patterns. Additional research with a larger and more diverse participant sample is required to confirm this conclusion

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

Placental transfusion and short-term outcomes among extremely preterm infants

ObjectiveTo compare short-term outcomes after placental transfusion (delayed cord clamping (DCC) or umbilical cord milking (UCM)) versus immediate cord clamping among extremely preterm infants.DesignRetrospective study.SettingThe Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network registry.PatientsInfants born <29 weeks' gestation in 2016 or 2017 without congenital anomalies who received active treatment after delivery.Intervention/exposureDCC or UCM.Main outcome measuresPrimary outcomes: (1) composite of mortality or major morbidity by 36 weeks' postmenstrual age (PMA); (2) mortality by 36 weeks PMA and (3) composite of major morbidities by 36 weeks' PMA. Secondary composite outcomes: (1) any grade intraventricular haemorrhage or mortality by 36 weeks' PMA and (2) hypotension treatment in the first 24 postnatal hours or mortality in the first 12 postnatal hours. Outcomes were assessed using multivariable regression, adjusting for mortality risk factors identified a priori, significant confounders and centre as a random effect.ResultsAmong 3116 infants, 40% were exposed to placental transfusion, which was not associated with the primary composite outcome of mortality or major morbidity by 36 weeks' PMA (adjusted OR (aOR) 1.26, 95% CI 0.95 to 1.66). However, exposure was associated with decreased mortality by 36 weeks' PMA (aOR 0.71, 95% CI 0.55 to 0.92) and decreased hypotension treatment in first 24 postnatal hours (aOR 0.66, 95% CI 0.53 to 0.82).ConclusionIn this extremely preterm infant cohort, exposure to placental transfusion was not associated with the composite outcome of mortality or major morbidity, though there was a reduction in mortality by 36 weeks' PMA.Trial registration numberNCT00063063