scholarly journals Application of multivariate logistic regression model to assess factors of importance influencing prevalence of abortion and stillbirth in Nigerian goat breeds

2014 ◽  
Vol 30 (1) ◽  
pp. 79-88 ◽  
Author(s):  
A. Yakubu ◽  
M.M. Muhammed ◽  
I.S. Musa-Azara
Keyword(s):  
Logistic Regression ◽  
Management Practices ◽  
Binary Logistic Regression ◽  
Multivariate Logistic Regression Model ◽  
Logistic Regression Models ◽  
Genetic Groups ◽  
Breed Group ◽  
Present Information ◽  
Indigenous Goat ◽  
Potential Factors

The aim of the study was to investigate the application of binary logistic regression to assess the potential factors associated with the prevalence of abortion and stillbirth in indigenous goat breeds in Nasarawa State, north central Nigeria. 5,268 kidding records of does from a total of 105 traditional goat herders from the year 2010-2011 were utilized in the study. The goats which were of West African Dwarf (WAD), Red Sokoto (RS), Sahel (SH) and WAD x RS crossbred (WR) genetic groups originated from different flocks and were reared under the traditional extensive system. The risk factors investigated were dam breed group, season, parity and number of foetuses. Of the 5,268 kidding records, 570 (10.8%) and 520 (9.87%) were cases of abortion and stillbirth, respectively. The logistic regression analysis revealed that season, parity and number of foetuses were the parameters of utmost importance (P<0.05) influencing the prevalence of abortion and stillbirth in the four genetic groups investigated. The logistic regression models were able to predict correctly 89.2 and 90.1% cases of abortion and stillbirth, respectively. The present information may be exploited in management practices to attenuate the incidence of abortion and stillbirth parturition, thereby increasing the productivity of the animals.

scholarly journals Antenatal care use and its determinants among migrant women during the first delivery: a nation-wide cross-sectional study in China

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xue Tang ◽  
Lulu Ding ◽  
Yuejing Feng ◽  
Yi Wang ◽  
Chengchao Zhou
Keyword(s):  
Logistic Regression ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Multivariate Logistic Regression Model ◽  
Migrant Women ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Primiparous Women

Abstract Background Reasonable use of antenatal care (ANC) services by pregnant women played a crucial role in ensuring maternal and child safety and reducing the risk of complications, disability, and death in mothers and their infants. This study aimed to investigate the ANC use, and to explore the factors associated with ANC use among migrant women during the first delivery in China. Methods This study used the data of National Health and Family Planning Commission of People Republic of China in 2014. A total of 1505 migrant primiparous women were included in our current analysis. Frequencies and proportions were used to describe the data. Chi-square tests and multivariate binary logistic regression models were performed to explore the determinants that affect the number of times migrant women used ANC during their first delivery. Results Of the 1505 participants, 279 (18.54%) women received the ANC less than 5 times, and 1226 (81.46%) women used the ANC at least 5 times during the first delivery. The multivariate logistic regression model showed that migrant primiparous women with college and above education(P < 0.05;OR = 2.57;95%CI = 1.19–5.55), from the households with higher monthly income (P < 0.01;OR = 2.01;95%CI = 1.30–3.13), covered by maternity insurance(P < 0.01;OR = 2.01;95%CI = 1.28–3.18), with maternal health records (P < 0.001;OR = 2.44;95%CI = 1.61–3.69), migrating across county (P < 0.05;OR = 2.57;95%CI = 1.14–5.81), having migration experience before pregnancy(P < 0.05;OR = 1.37;95%CI = 1.03–1.81) were more likely to use ANC for at least five times. Conclusions This study demonstrated that there were still some migrant maternal women (18.54%) who attended the ANC less than 5 times. Targeted policies should be developed to improve the utilization of ANC among migrant pregnant women.

scholarly journals Characterizing unsuccessful animal adoptions: age and breed predict the likelihood of return, reasons for return and post-return outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lauren Powell ◽  
Chelsea Reinhard ◽  
Donya Satriale ◽  
Margaret Morris ◽  
James Serpell ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Animal Behavior ◽  
Support Services ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Allocation Of Resources ◽  
Animal Shelters ◽  
Behavioral Issues ◽  
Logistic Regression Models ◽  
Breed Group

AbstractA considerable number of adopted animals are returned to animal shelters post-adoption which can be stressful for both the animal and the owner. In this retrospective analysis of 23,932 animal records from a US shelter, we identified animal characteristics associated with the likelihood of return, key return reasons, and outcomes post-return for dogs and cats. Binary logistic regression models were used to describe the likelihood of return, return reason and outcome based on intake age, intake type, sex, breed and return frequency. Behavioral issues and incompatibility with existing pets were the most common return reasons. Age and breed group (dogs only) predicted the likelihood of return, return reason and post-adoption return outcome. Adult dogs had the greatest odds of post-adoption return (OR 3.40, 95% CI 2.88–4.01) and post-return euthanasia (OR 3.94, 95% CI 2.04–7.59). Toy and terrier breeds were 65% and 35% less likely to be returned compared with herding breeds. Pit bull-type breeds were more likely to be returned multiple times (X2 = 18.11, p = 0.01) and euthanized post-return (OR 2.60, 95% CI 1.47–4.61). Our findings highlight the importance of animal behavior in the retention of newly adopted animals and provide useful direction for allocation of resources and future adoption counselling and post-adoption support services.

scholarly journals EPID-34. THE DETRIMENTAL EFFECT OF BIOPSY PRECEDING RESECTION IN SURGICALLY ACCESSIBLE GLIOBLASTOMAS: RESULTS FROM THE NATIONAL CANCER DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii85-ii86
Author(s):  
Ping Zhu ◽  
Xianglin Du ◽  
Angel Blanco ◽  
Leomar Y Ballester ◽  
Nitin Tandon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Failure Time ◽  
Binary Logistic Regression ◽  
Rank Test ◽  
Imaging Features ◽  
National Cancer Database ◽  
Prolonged Length ◽  
Logistic Regression Models ◽  
Operative Outcomes ◽  
The Impact

Abstract OBJECTIVES To investigate the impact of biopsy preceding resection compared to upfront resection in glioblastoma overall survival (OS) and post-operative outcomes using the National Cancer Database (NCDB). METHODS A total of 17,334 GBM patients diagnosed between 2010 and 2014 were derived from the NCDB. Patients were categorized into two groups: “upfront resection” versus “biopsy followed by resection”. Primary outcome was OS. Post-operative outcomes including 30-day readmission/mortality, 90-day mortality, and prolonged length of inpatient hospital stay (LOS) were secondary endpoints. Kaplan-Meier methods and accelerated failure time (AFT) models with gamma distribution were applied for survival analysis. Multivariable binary logistic regression models were performed to compare differences in the post-operative outcomes between these groups. RESULTS Patients undergoing “upfront resection” experienced superior survival compared to those undergoing “biopsy followed by resection” (median OS: 12.4 versus 11.1 months, log-rank test: P=0.001). In multivariable AFT models, significant survival benefits were observed among patients undergoing “upfront resection” (time ratio [TR]: 0.83, 95% CI: 0.75–0.93, P=0.001). Patients undergoing upfront GTR had the longest survival compared to upfront STR, GTR following STR, or GTR and STR following an initial biopsy (14.4 vs. 10.3, 13.5, 13.3, and 9.1, months), respectively (TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income and treatment at academic facilities were significantly associated with the likelihood of undergoing upfront resection after adjusting the covariates. Multivariable logistic regression revealed that 30-day mortality and 90-day mortality were decreased by 73% and 44% for patients undergoing “upfront resection” over “biopsy followed by resection”, respectively (both p &lt; 0.001). CONCLUSIONS Pre-operative biopsies for surgically accessible tumors with characteristic imaging features of Glioblastoma lead to worse survival despite subsequent resection compared to patients undergoing upfront resection.

scholarly journals Does Physical Fitness Predict Future Karate Success? A Study in Young Female Karatekas

2020 ◽  
Vol 15 (6) ◽  
pp. 868-873
Author(s):  
Óscar Martínez de Quel ◽  
Ignacio Ara ◽  
Mikel Izquierdo ◽  
Carlos Ayán
Keyword(s):  
Logistic Regression ◽  
Physical Fitness ◽  
Muscle Power ◽  
Binary Logistic Regression ◽  
Young Female ◽  
Binary Logistic Regression Analysis ◽  
Discriminative Ability ◽  
Logistic Regression Models ◽  
Elite Group ◽  
Competitive Success

Objective: To assess the discriminative ability of several fitness dimensions and anthropometric attributes for forecasting competitive success in female karate athletes. Methods: Fitness and anthropometric data from 98 female junior karatekas obtained during the training camps of the Spanish National Karate Federation between 1999 and 2012 were used. Binary logistic-regression models were built to ascertain whether the set of fitness and anthropometric variables could predict future sporting-performance levels. For this purpose, participants were classified as elite (medalist in World or European Championships in the senior category) or subelite (at least a medalist in Spanish National Championships in cadet or junior but not included in the elite group), according to the results achieved up to 2019. Results: Participants who were subsequently classified as elite karatekas showed significant differences in agility, upper- and lower-body muscle power, and general fitness in comparison with those who were classified as subelite in the senior category. A total of 57 junior female karatekas who were subsequently classified as elite (7) or subelite (50) were included in the binary logistic-regression analysis. Resultant models showed significant capacity to predict karate performance. Conclusions: Assessing physical fitness in junior categories can be a useful resource to determine future karate success. Coaches in this sport should pay special attention to the levels of muscle power and agility shown by their athletes, as both fitness dimensions could be indicators of future sportive success.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

Defining the Growth/No-Growth Interface for Listeria monocytogenes in Mexican-Style Cheese Based on Salt, pH, and Moisture Content

1999 ◽  
Vol 62 (6) ◽  
pp. 601-609 ◽  
Author(s):  
LANCE F. BOLTON ◽  
JOSEPH F. FRANK
Keyword(s):  
Logistic Regression ◽  
Listeria Monocytogenes ◽  
Binary Logistic Regression ◽  
Ordinal Logistic Regression ◽  
Growth Interface ◽  
Data Set ◽  
Logistic Regression Models ◽  
Binary Model ◽  
Fresh Cheese ◽  
Brine Concentration

The objective of this study was to define combinations of pH, salt, and moisture that produce growth, stasis, or inactivation of Listeria monocytogenes in Mexican-style cheese. A soft, directly acidified, rennet-coagulated, fresh cheese similar to Mexican-style cheese was produced. The cheese was subsequently altered in composition as required by the experimental protocol. A factorial design with four moisture contents (42, 50, 55, and 60%), four salt concentrations (2.0, 4.0, 6.0, and 8.0% wt/wt), six pH levels (5.0, 5.25, 5.50, 5.75, 6.0, and 6.5), and three replications was used. Observations of growth, stasis, or death were obtained for each combination after 21 and 42 days of incubation at 10°C. Binary logistic regression was used to develop an equation to determine the probability of growth or no growth for any combination within the range of the data set. In addition, ordinal logistic regression was used to calculate proportional odds ratios for growth, stasis, and death for each treatment combination. Ordinal logistic regression was also used to develop equations to determine the probability of growth, stasis, and death for formulations within the range of the data set. Models were validated with independently produced data. Of 60 samples formulated to have a 5% probability of Listeria growth (pH, 5.0 to 6.0; brine concentration, 8.17 to 16.00%), none supported growth. Of 30 samples formulated to have 50% probability of growth using the binary model (pH, 5.50 to 6.50; brine concentration, 3.23 to 12.50%), 20 supported growth. Of 30 samples formulated to have a 50% probability of growth according to the ordinal model (pH, 5.50 to 6.50; brine concentration, 3.37 to 10.90%), 16 supported growth. These data indicate that the logistic regression models presented accurately predict the behavior of L. monocytogenes in Mexican-style cheese.

Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

2019 ◽  
Vol 19 (4) ◽  
pp. 913-930 ◽  
Author(s):  
E. Keith Smith ◽  
Michael G. Lacy ◽  
Adam Mayer
Keyword(s):  
Logistic Regression ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Experimental Conditions ◽  
Logistic Regression Models ◽  
Nonlinear Regression Models ◽  
Size Number ◽  
Confidence Interval Coverage ◽  
The Difference

Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

scholarly journals The impact of demographic, clinical, genetic, and imaging variables on tau PET status

2020 ◽  
Author(s):  
Rik Ossenkoppele ◽  
◽  
Antoine Leuzy ◽  
Hanna Cho ◽  
Carole H. Sudre ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cortical Thickness ◽  
Regression Models ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Binary Logistic Regression ◽  
Clinical Genetic ◽  
Logistic Regression Models ◽  
Tau Pet ◽  
The Impact

Abstract Purpose A substantial proportion of amyloid-β (Aβ)+ patients with clinically diagnosed Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) are tau PET–negative, while some clinically diagnosed non-AD neurodegenerative disorder (non-AD) patients or cognitively unimpaired (CU) subjects are tau PET–positive. We investigated which demographic, clinical, genetic, and imaging variables contributed to tau PET status. Methods We included 2338 participants (430 Aβ+ AD dementia, 381 Aβ+ MCI, 370 non-AD, and 1157 CU) who underwent [18F]flortaucipir (n = 1944) or [18F]RO948 (n = 719) PET. Tau PET positivity was determined in the entorhinal cortex, temporal meta-ROI, and Braak V-VI regions using previously established cutoffs. We performed bivariate binary logistic regression models with tau PET status (positive/negative) as dependent variable and age, sex, APOEε4, Aβ status (only in CU and non-AD analyses), MMSE, global white matter hyperintensities (WMH), and AD-signature cortical thickness as predictors. Additionally, we performed multivariable binary logistic regression models to account for all other predictors in the same model. Results Tau PET positivity in the temporal meta-ROI was 88.6% for AD dementia, 46.5% for MCI, 9.5% for non-AD, and 6.1% for CU. Among Aβ+ participants with AD dementia and MCI, lower age, MMSE score, and AD-signature cortical thickness showed the strongest associations with tau PET positivity. In non-AD and CU participants, presence of Aβ was the strongest predictor of a positive tau PET scan. Conclusion We identified several demographic, clinical, and neurobiological factors that are important to explain the variance in tau PET retention observed across the AD pathological continuum, non-AD neurodegenerative disorders, and cognitively unimpaired persons.

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