scholarly journals Confidence Bands for Logistic Regression with Restricted Predictor Variables

Biometrics ◽  
1988 ◽  
Vol 44 (3) ◽  
pp. 739 ◽  
Author(s):  
Walter W. Piegorsch ◽  
George Casella
Keyword(s):  
Logistic Regression ◽  
Confidence Bands ◽  
Predictor Variables

Characteristics of Adenocarcinoma of the Gallbladder Following Cholecystectomies in the Hispanic Population

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S64-S65
Author(s):  
David Gustafson ◽  
Osvaldo Padilla
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Incidental Finding ◽  
Conditional Logistic Regression ◽  
Gallbladder Disease ◽  
Predictor Variables ◽  
Wall Thickening ◽  
Conditional Logistic Regression Analysis

Abstract Introduction Gallbladder adenocarcinoma (GBC) is a rare malignancy. Frequency of incidental adenocarcinoma of the gallbladder in the literature is approximately 0.2% to 3%. Typically, GBC is the most common type and is discovered late, not until significant symptoms develop. Common symptoms include right upper quadrant pain, nausea, anorexia, and jaundice. A number of risk factors in the literature are noted for GBC. These risk factors are also more prevalent in Hispanic populations. This study sought to compare patients with incidental gallbladder adenocarcinomas (IGBC) to those with high preoperative suspicion for GBC. Predictor variables included age, sex, ethnicity, radiologic wall thickening, gross pathology characteristics (wall thickness, stone size, stone number, and tumor size), histologic grade, and staging. Methods Cases of GBC were retrospectively analyzed from 2009 through 2017, yielding 21 cases. Data were collected via Cerner EMR of predictor variables noted above. Statistical analysis utilized conditional logistic regression analysis. Results The majority of patients were female (n = 20) and Hispanic (n = 19). There were 14 IGBCs and 7 nonincidental GBCs. In contrast with previous research, exact conditional logistic regression analysis revealed no statistically significant findings. For every one-unit increase in AJCC TNM staging, there was a nonsignificant 73% reduction in odds (OR = 0.27) of an incidental finding of gallbladder carcinoma. Conclusion This study is important in that it attempts to expand existing literature regarding a rare type of cancer in a unique population, one particularly affected by gallbladder disease. Further studies are needed to increase predictive knowledge of this cancer. Longer studies are needed to examine how predictive power affects patient outcomes. This study reinforces the need for routine pathologic examination of cholecystectomy specimens for cholelithiasis.

scholarly journals Predicting Hospitalization following Psychiatric Crisis Care using Machine Learning

2019 ◽  
Author(s):  
Matthijs Blankers ◽  
Louk F. M. van der Post ◽  
Jack J. M. Dekker
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Predictor Variables ◽  
Gradient Boosting ◽  
K Nearest Neighbors ◽  
Psychiatric Crisis ◽  
Crisis Care

Abstract Background: It is difficult to accurately predict whether a patient on the verge of a potential psychiatric crisis will need to be hospitalized. Machine learning may be helpful to improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate and compare the accuracy of ten machine learning algorithms including the commonly used generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact, and explore the most important predictor variables of hospitalization. Methods: Data from 2,084 patients with at least one reported psychiatric crisis care contact included in the longitudinal Amsterdam Study of Acute Psychiatry were used. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared. We also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis. Target variable for the prediction models was whether or not the patient was hospitalized in the 12 months following inclusion in the study. The 39 predictor variables were related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts. Results: We found Gradient Boosting to perform the best (AUC=0.774) and K-Nearest Neighbors performing the least (AUC=0.702). The performance of GLM/logistic regression (AUC=0.76) was above average among the tested algorithms. Gradient Boosting outperformed GLM/logistic regression and K-Nearest Neighbors, and GLM outperformed K-Nearest Neighbors in a Net Reclassification Improvement analysis, although the differences between Gradient Boosting and GLM/logistic regression were small. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions: Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was modest. Future studies may consider to combine multiple algorithms in an ensemble model for optimal performance and to mitigate the risk of choosing suboptimal performing algorithms.

scholarly journals Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data

2003 ◽  
Vol 93 (4) ◽  
pp. 428-435 ◽  
Author(s):  
E. D. De Wolf ◽  
L. V. Madden ◽  
P. E. Lipps
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Weather Conditions ◽  
Predictor Variables ◽  
Stepwise Logistic Regression ◽  
Head Blight ◽  
Logistic Regression Models

Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30°C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30°C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

Predicting Amputation and Multiple Debridements in Tooth Knuckle Injuries

2019 ◽  
Vol 24 (01) ◽  
pp. 6-12
Author(s):  
H.R. Smith ◽  
C. Conyard ◽  
J. Loveridge ◽  
R. Gunnarsson
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Predictor Variables ◽  
Delayed Presentation ◽  
Multivariate Logistic Regression ◽  
Outcome Measurements ◽  
Major Predictor ◽  
Laboratory Variables ◽  
The Relationship ◽  
Historic Cohort

Background: Tooth knuckle injuries can be expensive to treat and may necessitate amputation in some cases. Several limitations exist in the literature regarding our knowledge around the factors predicting amputation and the need for multiple debridements in treating this injury.Methods: A historic cohort study of 321 patients treated for tooth knuckle injuries was undertaken. Twenty-one demographic, clinical and laboratory variables were collected. Two outcome measurements were collected - the need for amputation and the need for more than one surgical debridement. A multivariate logistic regression was performed to determine the relationship between the predictor variables and the outcome measurements.Results: Of the 321 patients examined, 1.6% required amputations and 25% required multiple debridements. Osteomyelitis was found to be a major predictor for amputation in these patients (OR = 35). Delayed presentation (OR = 1.1) and diabetes (OR = 2.6) were found to significantly increase the risk of requiring multiple debridements.Conclusions: Our models were able to predict what patients were at the greatest risk for amputation and multiple debridement. Reducing rates of osteomyelitis and delays in presentation may help reduce the incidence of amputation and reoperation in this injury.

VR Service Patterns and Employment Outcomes of Transition-Aged Youth

2016 ◽  
Vol 47 (2) ◽  
pp. 20-26
Author(s):  
Gina Oswald
Keyword(s):  
Logistic Regression ◽  
Vocational Rehabilitation ◽  
Service Provision ◽  
Employment Outcomes ◽  
Predictor Variables ◽  
Competitive Employment ◽  
Service Patterns ◽  
Successful Employment ◽  
Managerial Positions

The purpose of this study was to descriptively explore the service provision of transition-aged youth in a state vocational rehabilitation (VR) agency and to determine if predictor variables could be identified for successful employment outcomes through logistic regression. At closure, more than half the participants were closed successfully in competitive employment. The majority were working in service, clerical and sales, or professional/technical/ managerial positions after receiving VR services focused on understanding the consumer's needs and creating appropriate plans, preparing for a job, obtaining a job and then retaining employment. Implications for transition and rehabilitation practice include the necessity o[specific transition-related training for VR counselors.

scholarly journals Bootstrap Logistic Regression on Determining Factors Affecting the Level of Entrepreneurial Capability

2019 ◽  
Vol 5 (1) ◽  
pp. 10-18
Author(s):  
Muhammad Ridho ◽  
Dodi Devianto
Keyword(s):  
Logistic Regression ◽  
Rural Area ◽  
Ordinal Scale ◽  
Predictor Variables ◽  
Response Variable ◽  
Factors Affecting ◽  
Bootstrap Estimation ◽  
Social Motive ◽  
Determining Factors ◽  
West Sumatra

The purpose of this study is to determine the factors that affect the level of entrepreneurial capability in tourism of rural area in Nagari Salayo of West Sumatra. The level of entrepreneurial capability is the response variable in this study with an ordinal scale consisting of four categories, they are lower, middle, high, or very high. Whereas the predictor variables consist of 4 socio-demographic factor variables, they are gender, education level, age group and occupation, and also 5 entrepreneurial motivation variables. To determine the predictor variables that are significantly affecting response variables, an ordinal logistic regression with a bootstrap estimation is executed. The study’s result shows two predictor variables that affect the response variable significantly, they are the entrepreneurial motive and social motive with the hit ratio of 61,667%. With that result, the model formed by bootstrapping logistic regression is able to determine the level of entrepreneurial capability in tourism of the rural area.

scholarly journals Predicting Loan Defaults Using Logistic Regression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Selena Zhao ◽  
Jiying Zou
Keyword(s):  
Logistic Regression ◽  
Exploratory Data Analysis ◽  
Predictor Variables ◽  
Loan Default ◽  
Stratified Sample ◽  
Anonymized Data ◽  
Exploratory Data ◽  
Loan Defaults ◽  
The Interest Rate ◽  
Made In

We used anonymized data from a loan company to analyze correlations between loan defaults and other characteristics of loans or borrowers of loans. We performed an exploratory data analysis of the different factors and how they correlated with loan defaults. Using observations made in the EDA, we proceeded to use logistic regression to predict the odds of loan defaults with several loan characteristics as predictor variables. Different models were evaluated and cross-validated using AIC, AUC, and predicted accuracy. Weighted accuracy was also measured because the loan dataset was a stratified sample. We concluded that the interest rate most accurately predicted the odds of a loan default and that the most useful model was both simplistic and accurate. Research was limited by the variables that were not analyzed during EDA, the limited variables the loan dataset contained, and the modeling technique used.

scholarly journals Factors That Determine the Likelihood of Giving Birth to the First Child within 10 Months after Marriage

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Abdul-Karim Iddrisu ◽  
Francis Kwame Bukari ◽  
Kwaku Opoku-Ameyaw ◽  
Gabriel Oppong Afriyie ◽  
Kassim Tawiah
Keyword(s):  
Higher Education ◽  
Risk Factors ◽  
Logistic Regression ◽  
Logistic Regression Model ◽  
Contraceptive Use ◽  
Birth Length ◽  
Predictor Variables ◽  
First Child ◽  
Unit Increase ◽  
Length Variable

Background. One of the major aims of marriage is to procreate or give birth to a child. Childbirth is so crucial in marriage that it often determines the happiness of the couple. Too much delay in childbirth after marriage or the likelihood that one cannot give birth after marriage can lead to divorce. However, causes of delay in childbirth are often difficult to detect by both the Gynaecologist and the couple involved. This makes proposing solutions to issues related to childbirth usually unsuccessful. Methods. It is against this background that we conducted this study to identify factors that determine childbirth within 10 months or after 10 months of marriage (birth length) among women in Ghana. This was achieved by using a logistic regression model for the dichotomous birth length variable, adjusting for risk factors/predictors of birth length. The data used for the study were obtained from the 2014 Ghana Demographic and Health Survey, consisting 6,525 complete cases with 18 predictor variables. Statistical analyses were carried out using STATA version 14.1. Results. The results show that respondents who have ever terminated pregnancy are more likely (OR=0.178, 95%CI=0.044, 0.312) to deliver after 10 months, wives whose husbands have higher education are less likely (OR=‐0.162, 95%CI=‐0.236, ‐0.088) to give birth after 10 months of marriage, wives who reported that beating is justified if she goes out without her husband’s notice are more likely (OR=0.466, 95%CI=0.305, 0.628) to give birth after 10 months, wives who reported that beating is justified if she neglects the child are more likely (OR=‐0.305, 95%CI=‐0.461, ‐0.149) to give birth within 10 months, and wives who reported that beating is justified when she argues with her husband are less likely (OR=‐0.301, 95%CI=‐0.451, ‐0.152) to give birth after 10 months of marriage. Every unit increase in the age of the respondent at marriage increases the likelihood of giving birth after 10 months of marriage, and a unit increase in the age of the respondent at first sex decreases the likelihood of giving birth after 10 months in marriage. Conclusions. For conception within 1 month of marriage, wives and husbands should/are encouraged to have frequent sex, any negative social behaviour or policies must be discouraged, experts’ advice on contraceptive use must be sought, and women are encouraged to desist from termination of pregnancy at any time of their life. Husbands should openly express their desire and love for their children since this increases the likelihood of wives’ desire to give birth. This leads to frequent sex, which then reduces conception time, and hence childbirth within the shortest possible time.

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