ASSESSING LOGISTIC REGRESSION BY BOOTSTRAPPING AND MONTE CARLO SIMULATION: MODELING LOW BIRTH WEIGHT

2019 ◽  
Vol 16 (2) ◽  
pp. 13-30
Author(s):  
Yunfei Wang ◽  
Dejian Lai
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Simulation Modeling

scholarly journals Cheating at Craps

2021 ◽  
Vol 48 (4) ◽  
pp. 53-61
Author(s):  
Andrea Marin ◽  
Carey Williamson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Modeling And Simulation ◽  
Analytical Model ◽  
Quantitative Evaluation ◽  
Mathematical Analysis ◽  
Simulation Modeling ◽  
Analytical Modeling ◽  
The World ◽  
Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

scholarly journals Monte Carlo Simulation Modeling of a Regional Stroke Team's Use of Telemedicine

2015 ◽  
Vol 23 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Elham Torabi ◽  
Craig M. Froehle ◽  
Christopher J. Lindsell ◽  
Charles J. Moomaw ◽  
Daniel Kanter ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Modeling

Serum Bilirubin Levels, Intracranial Hemorrhage, and the Risk of Developmental Problems in Very Low Birth Weight Neonates

PEDIATRICS ◽  
1992 ◽  
Vol 90 (6) ◽  
pp. 888-892
Author(s):  
T. Michael O'Shea ◽  
Robert G. Dillard ◽  
Kurt L. Klinepeter ◽  
Donald J. Goldstein
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Intracranial Hemorrhage ◽  
Very Low Birth Weight ◽  
Confounding Factors ◽  
Developmental Problems ◽  
Period Increase ◽  
Developmental Index ◽  
Mental Developmental Index

To study whether elevated levels of bilirubin in the neonatal period increase the risk of developmental problems for very low birth weight neonates, the investigators used data from a geographically based sample of 495 very low birth weight neonates, born January 1, 1985, to December 31, 1989, who survived to 1 year of adjusted age. Maximum neonatal bilirubin levels were found in medical records. A developmental problem was defined as either cerebral palsy or a Bayley Mental Developmental Index of less than 68 at 1 year adjusted age. Potentially confounding factors were controlled using logistic regression. To control for the effects of intracranial abnormalities (eg, intraventricular hemorrhage), separate logistic regression analyses were carried out for three strata, defined according to the results of cranial ultrasonography. In these analyses, the following odds ratios (with 95% confidence limits) were found for the association of maximum neonatal bilirubin concentration and developmental problems: for subjects without intracranial abnormalities, 0.9 (0.7, 1.9); for subjects with uncomplicated intracranial hemorrhage, 1.5 (0.8, 2.5); for subjects with complicated intracranial hemorrhage or intraparenchymal echodensities, 1.2 (0.4, 3.6). In summary, in analyses controlled for confounding factors, maximum neonatal bilirubin level was not consistently associated with the risk of developmental problems identifiable at 1 year.

scholarly journals Clinical profile and factors determining outcome of intramural very low birth weight babies in a tertiary care centre: a retrospective study

2017 ◽  
Vol 5 (2) ◽  
pp. 520
Author(s):  
Devi Meenakshi K. ◽  
Arasar Seeralar A. T. ◽  
Srinivasan Padmanaban
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Logistic Regression Analysis ◽  
Very Low Birth Weight ◽  
Tertiary Care ◽  
P Value ◽  
Increased Risk

Background: Very low birth weight (VLBW) babies are at increased risk of a number of complications both immediate and late. Worldwide it has been observed that these babies contribute to a significant extent to neonatal mortality and morbidity. Aim of the study was to study the risk factors contributing to mortality in VLBW babies and to evaluate the morbidity pattern in these infants.Methods: A retrospective analysis of data retrieved from the case records of VLBW babies admitted in the NICU of Kilpauk Medical College between January 2015 to December 2015. Out of the 2360 intramural babies admitted during the study period, 99 babies were less than 1500 gms. The risk factors for these babies were analyzed for their association with the outcome. Data were statistically analyzed.Results: In present study, we found that sex of the baby, gestational age, obstetric score, birth asphyxia, pulmonary haemorrhage, ROP and presence of shock were found to be associated with increased mortality. By logistic regression analysis it was observed that birth weight of the baby (p value 0.002), duration of stay (p value 0.0006), presence of shock (p<0.0001), were the risk factors significantly associated with poor outcome.Conclusions: Among the maternal and neonatal factors analyzed in the study using logistic regression analysis, birth weight, duration of hospital stay and presence of shock were significantly related to poor outcome. Of these presence of shock was the single most important factor that predicted increased mortality.

scholarly journals Stunting at birth and associated factors among newborns delivered at the University of Gondar Comprehensive Specialized Referral Hospital

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245528
Author(s):  
Almaz Tefera Gonete ◽  
Bogale Kassahun ◽  
Eskedar Getie Mekonnen ◽  
Wubet Worku Takele
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Short Stature ◽  
Odds Ratio ◽  
Associated Factors ◽  
Adjusted Odds Ratio ◽  
Referral Hospital ◽  
Factors Associated ◽  
The University

Background Stunting at birth is a chronic form of undernutrition majorly attributable to poor prenatal nutrition, which could persist in children’s later life and impact their physical and cognitive health. Although multiple studies have been conducted in Ethiopia to show the magnitude of stunting and factors, all are concentrated on children aged between 6 to 59 months. Therefore, this study was done to determine the prevalence and associated factors of stunting at birth among newborns delivered at the University of Gondar Comprehensive Specialized Referral Hospital, Northwest, Ethiopia. Methods An institution-based cross-sectional study was conducted from February 26th to April 25th/2020. A systematic random sampling technique was used, to select a total of 422 newborn-mother pairs. The binary logistic regression was employed to identify factors associated with stunting and all independent variables were entered into the multivariable logistic regression model to adjust for confounders. Variables that had significant association were identified based on p-value < 0.05 and the adjusted odds ratio with its respective 95% confidence interval was applied to determine the strength as well as the direction of the association. Results About 30.5% (95% CI: 26.3%, 35.1%) of newborns were stunted at birth. Being male [Adjusted odds ratio (AOR) = 2.9(1.62, 5.21)], newborns conceived in Kiremt(rainy season) [AOR = 2.7(1.49, 4.97)], being low birth weight [AOR = 3.1(1.64, 6.06)] were factors associated with stunting at birth. Likewise, newborns born to short stature mothers [AOR = 2.8(1.21, 6.62)] and chronically malnourished mothers [AOR = 15.3(8.12, 29.1)] were at greater risk of being stunted. Conclusion Just under a third of newborns are stunted at birth, implying a pressing public health problem. Newborns born to chronically malnourished and short stature mothers were more stunted. Besides, stunting was prevalently observed among male neonates, newborns conceived in Kiremet, and being low birth weight. Thus, policymakers and nutrition programmers should work on preventing maternal undernutrition through nutrition education to reduce the burden of low birth weight and stunting. Further, paying due attention to newborns conceived in Kiremet season to improve nutritional status is recommended.

scholarly journals Magnitude of Low Birth Weight and Associated Factors among Newborns Delivered in Dangla Primary Hospital, Amhara Regional State, Northwest Ethiopia, 2017

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Asmare Talie ◽  
Mekuanint Taddele ◽  
Mulunesh Alemayehu
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Food Intake ◽  
Low Birth Weight ◽  
Associated Factors ◽  
Previous History ◽  
P Value ◽  
Future Health ◽  
Additional Food ◽  
Primary Hospital

Background. Low birth weight is defined as a live born infant weighs less than 2 500 g regardless of gestational age. Globally, the prevalence of low birth weight ranges from 3% to 15%. Birth weight plays an important role in infant mortality, morbidity, development, and future health. The prevalence of low birth weight in Ethiopia was estimated to be 14% which is one of the highest percentages in the world. So, the aim of this study is to assess magnitude and associated factors of low birth weight among newborns delivered at Dangla Primary Hospital, Amhara Region, Ethiopia. Methods. An institution-based cross-sectional study was conducted at Dangla Primary Hospital from September 27 to June 10, 2017. Systematic random sampling technique was used to select the 232 study participants. A structured and pretested questionnaire was used to collect data. Data quality was assured by pretesting, training, and frequent supervision. Descriptive statistics were performed for the descriptive part of the study. Binary and multiple logistic regression analyses were conducted to identify independent predictors of low birth weight. Those variables and p-value < 0.25 were included in the multivariable logistic regression for controlling the possible effect of confounders. Finally, variables which had significant association were identified on the basis of AOR, with 95%CI and with P-value <0.05. Results. Magnitude of low birth weight was 10.3 %. Previous history of low birth weight [AOR = 3.2, 95% CI: (1.13-9.9)], additional food intake during the last pregnancy [AOR = 5.0, 95% CI: (1.2-16.2)], and preterm delivery [AOR = 2.1, 95% CI: (3.1-19.2)] were independent predictors of low birth weight. Conclusion. Magnitude of low birth weight in Dangla Primary Hospital was high. So, strengthening counseling systems for women through quality antenatal care on advantage of additional food intake and previous bad obstetric outcome is necessary to alleviate the delivery of low birth weight neonates in the study area.

scholarly journals The Prevalence and Associated Factors of Neonatal Hypothermia within Six Hours of Birth among Newborns in Pokhara

2020 ◽  
Vol 2 (2) ◽  
pp. 1150-120
Author(s):  
Rita Adhikari ◽  
Radhika Regmi ◽  
Babita Subedi
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Antenatal Care ◽  
Associated Factors ◽  
Multivariate Logistic Regression Analysis ◽  
Pulmonary Hemorrhage ◽  
Bivariate Analysis ◽  
Multivariate Logistic Regression ◽  
Neonatal Hypothermia

Background: Hypothermia is an important cause of neonatal morbidity and mortality. Persistent hypothermia leads to cold injury that results edema, scleroderma, pulmonary hemorrhage, jaundice and death. The objective of this study was to identify the prevalence and associated factors of neonatal hypothermia among newborns within six hours of Birth in Pokhara. Methods: The institutional based quantitative descriptive cross sectional study was done among 402 systematic randomly selected respondents by using structured format and digital thermometer MT 100 after taking ethical approval from Nepal Health Research Council and Pokhara Academy of Health Sciences. Data was analyzed by using SPSS version 20. Multivariate logistic regression analysis was done for the variables (p<0.25) in bivariate analysis. The variables (p< 0.05) with Adjusted Odds ratio (AOR) at 95% CI in the multivariate logistic regression was considered as independent associated variables. Results: The prevalence of neonatal hypothermia in the study area was about 43 percent. The significant independent factors of neonatal hypothermia were maternal age after 35 years (p=0.03, AOR: 4.087, 95% CI: 1.12-14.97), inadequate antenatal care (p=0.03, AOR: 0.52, 95% CI: 0.29-0.94), low birth weight (p=0.00, AOR: 0.433, 95% CI: 0.24- 0.77) and resuscitated babies at birth (p =0.00, AOR: 3.808)95% CI: 1.69- 4.65). Conclusion: Out of 10 births, four babies were hypothermic in study site. Mother’s age more than 35 years, inadequate antenatal care, low birth weight and resuscitation at birth were associated factors of Neonatal hypothermia. So, special care is necessary for low birth weight babies and during resuscitation.

scholarly journals Comparison between the maximum likelihood and the bayesian estimation methods for logistic regression model (case study: risk of low birth weight in Indonesia)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012001
Author(s):  
P R Sihombing ◽  
S R Rohimah ◽  
A Kurnia
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Low Birth Weight ◽  
Regression Model ◽  
Bayesian Estimation ◽  
Logistic Regression Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Model Case

Abstract This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.

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