Comparing Penalized Regression Analysis of Logistic Regression Model with Multicollinearity

2019 ◽  
Author(s):  
Autcha Araveeporn ◽  
Choojai Kuharatanachai
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Penalized Regression

scholarly journals Understanding Primary Ciliary Dyskinesia: Experience From a Mediterranean Diagnostic Reference Centre

2020 ◽  
Vol 9 (3) ◽  
pp. 810
Author(s):  
Miguel Armengot-Carceller ◽  
Ana Reula ◽  
Manuel Mata-Roig ◽  
Jordi Pérez-Panadés ◽  
Lara Milian-Medina ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Situs Inversus ◽  
Primary Ciliary Dyskinesia ◽  
Logistic Regression Model ◽  
Productive Cough ◽  
Simple Logistic ◽  
Ciliary Dyskinesia

Background: Due to the lack of a gold standard diagnostic test, reference centres with experienced personnel and costly procedures are needed for primary ciliary dyskinesia (PCD) diagnostics. Diagnostic flowcharts always start with clinical symptoms. Therefore, the aim of this work is to define differential clinical criteria so that only patients clinically compatible with PCD are referred to reference centres. Materials and methods: 18 variables from 476 Mediterranean patients with clinically suspicious PCD were collected. After analysing cilia function and ultrastructure, 89 individuals were diagnosed with PCD and 387 had a negative diagnosis. Simple logistic regression analysis, considering PCD as a dependent variable and the others as independent variables, was done. In order to define the variables that best explain PCD, a step-wise logistic regression model was defined. Aiming to classify individuals as PCD or PCD-like patients, based on variables included in the study, a classification and regression tree (CART) was designed. Results and conclusions: Simple logistic regression analysis shows statistically significant association between age at the beginning of their symptomatology, periodicity, fertility, situs inversus, recurrent otitis, atelectasis, bronchiectasis, chronic productive cough, rhinorrea, rhinusinusitis and recurrent pneumonias, and PCD. The step-wise logistic regression model selected situs inversus, atelectasis, rhinorrea, chronic productive cough, bronchiectasis, recurrent pneumonias, and otitis as PCD predictive variables (82% sensitivity, 88% specificity, and 0.92 Area Under the Curve (AUC)). A decision tree was designed in order to classify new individuals based on pansinusitis, situs inversus, periodicity, rhinorrea, bronchiectasis, and chronic wet cough.

scholarly journals Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

2020 ◽  
Author(s):  
Mayssa Traboulsi ◽  
Zainab El Alaoui Talibi ◽  
Abdellatif Boussaid
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Preterm Birth ◽  
Regression Model ◽  
Low Income ◽  
Logistic Regression Model ◽  
Penalized Regression ◽  
Low Income Countries ◽  
Nulliparous Women ◽  
Multiparous Women

Abstract Background: Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Multiparous women in our study presented a higher PTB prevalence compared to nulliparous women. Methods: In a cohort study from Northern Lebanon of 1996 women, 922 were multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. We compared 4 modified logistic regression models (up-sampling, lasso penalized regression) to develop a nomogram that can screen for preterm in multi-parous women. The models were trained and validated on different data sets.Results: The best PTB prediction of the Logistic regression model reached around 88%. This was obtained using a Logistic Regression Model trained on up-sampled datasets and LASSO (Least Absolute Shrinkage and Selection Operator) penalized. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. Conclusions: The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. This tool will allow physicians to screen women that represent a high risk for spontaneous preterm birth and run furthermore adequate additional tests leading to better medical surveillance that can reduce PTB incidence.

scholarly journals Determinants of acute respiratory infection among under-five children in rural Ethiopia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Amanuel Mengistu Merera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Logistic Regression Model ◽  
P Value ◽  
Under Five ◽  
Under Five Children ◽  
Rural Ethiopia

Abstract Introduction In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 to 33.5%. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods A cross-sectional study involving 7911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results The total ARI prevalence rate among 7911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8%), followed by Tigray (12.7%), with the lowest frequency found in Benishangul Gumuz (2.4%). A multivariable logistic regression model revealed that child from Poor household (AOR = 2.170, 95% CI: 1.631–2.887), mother’s no education (AOR = 2.050,95% CI: 1.017–4.133), mother’s Primary education (AOR = 2.387, 95% CI:1.176–4.845), child had not received vitamin A (AOR = 1.926, 95% CI:1.578–2.351), child had no diarrhea (AOR = 0.257, 95% CI: 0.210–0.314), mothers not working (AOR = 0.773, 95% CI:0.630–0.948), not stunted (AOR = 0.663, 95% CI: 0.552–0.796), and not improved water source (AOR = 1.715, 95% CI: 1.395–2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

scholarly journals Determinants of Acute Respiratory Infection among Under-Five Children in Rural Ethiopia

2021 ◽  
Author(s):  
Amanuel Mengistu Merera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Logistic Regression Model ◽  
P Value ◽  
Under Five ◽  
Under Five Children ◽  
Rural Ethiopia

Abstract Introduction: In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 % to 33.5 %. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods: A cross-sectional study involving 7,911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results: The total ARI prevalence rate among 7,911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8 %), followed by Tigray (12.7 %), with the lowest frequency found in Benishangul Gumuz (2.4 %). A multivariable logistic regression model revealed that child from Poor household (AOR=2.170, 95% CI: 1.631-2.887), mother’s no education (AOR=2.050,95% CI: 1.017-4.133), mother’s Primary education (AOR=2.387, 95% CI:1.176-4.845), child had not received vitamin A (AOR=1.926, 95% CI:1.578-2.351), child had no diarrhea (AOR=0.257, 95% CI: 0.210-0.314), mothers not working (AOR=0.773, 95% CI:0.630-0.948), not stunted (AOR=0.663, 95% CI: 0.552-0.796), and not improved water source (AOR=1.715, 95% CI: 1.395-2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions: Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

scholarly journals LOGISTIC REGRESSION ON THE PRIMIPARAL AGE OF WOMEN IN GOMBE, GOMBE STATE

2020 ◽  
Vol 5 (7) ◽  
pp. 7-16
Author(s):  
A.U. Kinafa ◽  
M.B. Mohammed ◽  
A. Abdulkadir
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Logistic Regression Model ◽  
Healthcare Providers ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Challenging Problem ◽  
First Child

Failure of women to undergo a successful first child delivery is becoming one of the most challenging problem and a major concern to most of our healthcare providers. In this paper, we apply the binary logistic regression analysis to investigate whether age of women at first birth have a relationship with the outcome of their delivery (Success or failure). The data was collected from Gombe Town Maternity and was subjected to analysis. From the result of the analysis, we observed that most of the women at tender age (12-17) are classified to fail (69%) during their first child delivery while most of the women at higher age (19 and above) have a better chance of succeeding during their first parturition. Also, the result shows that the average age at which women ought to conceive successfully is 19 years. The Wald statistics result also shows that the logistic regression model fits the data very well.

scholarly journals Factors Affecting Postnatal Care in Bangladesh: Clustered Data Analysis

2018 ◽  
Vol 66 (1) ◽  
pp. 59-65
Author(s):  
Mehejabeen Mahbub ◽  
Most Fatima Tuz Zahura
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Data Analysis ◽  
Antenatal Care ◽  
Regression Model ◽  
Birth Order ◽  
Postnatal Care ◽  
Logistic Regression Model ◽  
Wealth Index ◽  
Factors Affecting

The study aims to determine the factors affecting postnatal care in Bangladesh using the data extracted from Bangladesh Demographic and Health Survey (BDHS), 2014. For the purpose of regression analysis, mixed logistic regression model has been utilized to take into account the possible correlation among subjects within clusters. It is found that region, place of residence, mother’s education, wealth index, access to media, birth order and antenatal care visits have significant association with postnatal care. Dhaka Univ. J. Sci. 66(1): 59-65, 2018 (January)

scholarly journals Nomogram to Early Screen Multiparous Women for Preterm Birth in a Cohort Study

2020 ◽  
Author(s):  
Mayssa Traboulsi ◽  
Zainab E. El Alaoui- Talibi ◽  
Abdellatif Boussaid
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Preterm Birth ◽  
Regression Model ◽  
Low Income ◽  
Logistic Regression Model ◽  
Penalized Regression ◽  
Low Income Countries ◽  
Nulliparous Women ◽  
Multiparous Women

Abstract Background: Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Multiparous women in our study presented a higher PTB prevalence compared to nulliparous women. Methods: In a cohort study from Northern Lebanon of 1996 women, 922 were multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. We compared 4 modified logistic regression models (up-sampling, lasso penalized regression) to develop a nomogram that can screen for preterm in multi-parous women. The models were validated on a separate set of data.Results: The best PTB prediction of the Logistic regression model reached around 88%. This was obtained using a Logistic Regression Model trained on up-sampled datasets and LASSO (Least Absolute Shrinkage and Selection Operator) penalized. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. Conclusions: The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. This tool will allow physicians to screen women and run furthermore adequate additional tests leading to better medical surveillance that can reduce PTB incidence.

scholarly journals The Determinant Factors of Muzakki Toward Paying Zakat, Infaq, Sedekah (ZIS) in Zakat Institutions

2020 ◽  
Vol 3 (2) ◽  
pp. 143
Author(s):  
Hening Pratika Nila Hapsari ◽  
Unggul Priyadi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Logistic Regression Model ◽  
Primary Data ◽  
Role Of Government ◽  
Financial Accountability ◽  
Objective Study

Introductions to The Problem: Zakat is one of worship which is often mentioned in the Al Quran. It's just that the potential for Zakat, Infaq, Alms (ZIS) is not comparable to the actual actual figures. Many factors influence muzakki in paying ZIS.Purpose/Objective Study: This study aims to analyze the factors that influence muzakki to pay ZIS in zakat institutions, namely Yatim MandiriDesign/ Methodology/ Approach: The sample in this study amounted to 200 respondents. LAZ Yatim Mandiri was chosen because it is an Amil Zakat Institution that is consistent in collecting ZIS funds from the smallest amount to the large amount. This study uses logistic regression analysis and the data used are primary data. Based on the analysis that has been done, it is found that 61% results can be predicted correctly in the logistic regression model in this study.Findings: The consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is influenced by the variables of religiosity, income, trust, shariah compliance, knowledge, justice, data publication, financial accountability, motivation, the role of ulama, the role of government. And the consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is not influenced by the variables of shariah compliance and financial accountability.

scholarly journals Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao-ran Zhang ◽  
Ming-you Xu ◽  
Xiong-gang Yang ◽  
Feng Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

IntroductionVenous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.MethodsWe retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.ResultsIn the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.ConclusionThe prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

Polytomous logistic regression analysis of the General Health Questionnaire and the Present State Examination

1989 ◽  
Vol 19 (3) ◽  
pp. 755-764 ◽  
Author(s):  
F. W. Wilmink ◽  
T. A. B. Snijders
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Present State ◽  
General Health ◽  
Logistic Regression Model ◽  
General Health Questionnaire ◽  
Health Questionnaire ◽  
Polytomous Logistic Regression ◽  
State Examination

SynopsisFirst, two examples of dichotomous logistic regression analysis are presented. The probability of being a psychiatric case according to the Present State Examination is predicted from the total score on the General Health Questionnaire and from the general practitioner's judgement on the presence of a mental health problem. Subjects were 292 primary care attenders. Results are compared with those from prior studies.Next, the extension to the polytomous case is demonstrated. The probability of being at any given level of the Index of Definition (computed from PSE data) is estimated from the General Health Questionnaire total score by an ordered polytomous logistic regression model. Several applications of the polytomous logistic regression model are discussed. These range from estimating the proportion of psychiatric cases among individuals who refuse to be interviewed to the formulation of sampling schemes which can be expected to reduce costs while at the same time yielding optimal information for testing specific hypotheses.

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