scholarly journals Incidence and risk factors of new-onset hypertrophic pachymeningitis in patients with anti-neutrophil antibody-associated vasculitis: using logistic regression and classification tree analysis

2018 ◽  
Vol 38 (4) ◽  
pp. 1039-1046 ◽  
Author(s):  
Aya Imafuku ◽  
Naoki Sawa ◽  
Masahiro Kawada ◽  
Rikako Hiramatsu ◽  
Eiko Hasegawa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Classification Tree ◽  
Classification Tree Analysis ◽  
Hypertrophic Pachymeningitis ◽  
Tree Analysis ◽  
New Onset

Substance Use Relapse Among Veterans at Termination of Treatment for Substance Use Disorders

2021 ◽  
Author(s):  
Christian A Betancourt ◽  
Panagiota Kitsantas ◽  
Deborah G Goldberg ◽  
Beth A Hawks
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
Logistic Regression ◽  
Substance Use Disorders ◽  
Classification Tree ◽  
Treatment Completion ◽  
Classification Tree Analysis ◽  
Data Set ◽  
Tree Analysis ◽  
Substance Addiction

ABSTRACT Introduction Military veterans continue to struggle with addiction even after receiving treatment for substance use disorders (SUDs). Identifying factors that may influence SUD relapse upon receiving treatment in veteran populations is crucial for intervention and prevention efforts. The purpose of this study was to examine risk factors that contribute to SUD relapse upon treatment completion in a sample of U.S. veterans using logistic regression and classification tree analysis. Materials and Methods Data from the 2017 Treatment Episode Data Set—Discharge (TEDS-D) included 40,909 veteran episode observations. Descriptive statistics and multivariable logistic regression analysis were conducted to determine factors associated with SUD relapse after treatment discharge. Classification trees were constructed to identify high-risk subgroups for substance use after discharge from treatment for SUDs. Results Approximately 94% of the veterans relapsed upon discharge from outpatient or residential SUD treatment. Veterans aged 18-34 years old were significantly less likely to relapse than the 35-64 age group (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.66, 0.82), while males were more likely than females to relapse (OR 1.55, 95% CI: 1.34, 1.79). Unemployed veterans (OR 1.92, 95% CI: 1.67, 2.22) or veterans not in the labor force (OR 1.29, 95% CI: 1.13, 1.47) were more likely to relapse than employed veterans. Homeless vs. independently housed veterans had 3.26 (95% CI: 2.55, 4.17) higher odds of relapse after treatment. Veterans with one arrest vs. none were more likely to relapse (OR 1.52, 95% CI: 1.19, 1.95). Treatment completion was critical to maintain sobriety, as every other type of discharge led to more than double the odds of relapse. Veterans who received care at 24-hour detox facilities were 1.49 (95% CI: 1.23, 1.80) times more likely to relapse than those at rehabilitative/residential treatment facilities. Classification tree analysis indicated that homelessness upon discharge was the most important predictor in SUD relapse among veterans. Conclusion Aside from numerous challenges that veterans face after leaving military service, SUD relapse is intensified by risk factors such as homelessness, unemployment, and insufficient SUD treatment. As treatment and preventive care for SUD relapse is an active field of study, further research on SUD relapse among homeless veterans is necessary to better understand the epidemiology of substance addiction among this vulnerable population. The findings of this study can inform healthcare policy and practices targeting veteran-tailored treatment programs to improve SUD treatment completion and lower substance use after treatment.

scholarly journals Diminished ovarian reserve is the major cause of infertility among women undergoing their first assisted reproductive technology treatment: a retrospective observational study

2020 ◽  
Author(s):  
Mengqian Zhang ◽  
Hongxian Zhang ◽  
Rui Yang ◽  
Guoshuang Feng ◽  
Huiyu Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ovarian Reserve ◽  
Cox Regression ◽  
Classification Tree ◽  
Diminished Ovarian Reserve ◽  
Classification Tree Analysis ◽  
Success Rates ◽  
Tree Analysis ◽  
Cox Regression Analysis

Abstract Background This study aims to investigate the effects of various factors on treatment outcomes in women undergoing in vitro fertilization or intracytoplasmic sperm injection (IVF/ICSI) with embryo transfer (ET). Methods Of the 8993 eligible women who underwent their first IVF/ICSI–ET cycles, and met our inclusion and exclusion criteria, 2742(30.5%) achieved clinical pregnancy while 6251(69.5%) did not. Multivariable Cox regression analysis, multiple logistic regression analysis, and classification tree analysis were used sequentially to screen key predictors among predictors of various infertility causes and ovarian stimulation protocols through the best subset technique. Results Multivariate Cox regression analysis showed that the main factor affecting fertility in first attempts at IVF/ICSI–ET is diminished ovarian reserve (DOR), with a hazard ratio (HR) of 0.406 and 95% confidence interval (CI) of 0.353–0.466. Multiple forward logistic regression with 5-fold cross-validation also found that, with an odds ratio (OR) of 2.522 (95% CI = 2.167–2.937), DOR affects fertility. The classification tree analysis was further used to better visualize the model. Conclusions DOR is the major factor affecting success rates in couples undergoing their first attempt at IVF/ICSI-ET. The selection of the most appropriate pairs for IVF/ICSI treatment can not only increase the success rates but also the cumulative cost-effectiveness.

scholarly journals A Comparison of Logistic Regression and Classification Tree Analysis for Behavioural Case Linkage

2012 ◽  
Vol 9 (3) ◽  
pp. 235-258 ◽  
Author(s):  
Matthew Tonkin ◽  
Jessica Woodhams ◽  
Ray Bull ◽  
John W. Bond ◽  
Pekka Santtila
Keyword(s):  
Logistic Regression ◽  
Classification Tree ◽  
Classification Tree Analysis ◽  
Tree Analysis

Classification tree analysis to examine influences on colorectal cancer screening

2015 ◽  
Vol 26 (3) ◽  
pp. 443-454 ◽  
Author(s):  
Gregory M. Dominick ◽  
Mia A. Papas ◽  
Michelle L. Rogers ◽  
William Rakowski
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Classification Tree ◽  
Classification Tree Analysis ◽  
Tree Analysis

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

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