scholarly journals Response to Fluvoxamine in the Obsessive-Compulsive Disorder Patients: Bayesian Ordinal Quantile Regression

2021 ◽  
Vol 17 (1) ◽  
pp. 146-151
Author(s):  
Samad Safiloo ◽  
Yadollah Mehrabi ◽  
Sareh Asadi ◽  
Soheila Khodakarim
Keyword(s):  
Logistic Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Marital Status ◽  
Obsessive Compulsive Disorder ◽  
Regression Models ◽  
Obsessive Compulsive ◽  
Logistic Regression Models ◽  
Compulsive Disorder ◽  
Quantile Regression Model

Background: Obsessive-Compulsive Disorder (OCD) is a chronic neuropsychiatric disorder associated with unpleasant thoughts or mental images, making the patient repeat physical or mental behaviors to relieve discomfort. 40-60% of patients do not respond to Serotonin Reuptake Inhibitors, including fluvoxamine therapy. Introduction: The aim of the study is to identify the predictors of fluvoxamine therapy in OCD patients by Bayesian Ordinal Quantile Regression Model. Methods: This study was performed on 109 patients with OCD. Three methods, including Bayesian ordinal quantile, probit, and logistic regression models, were applied to identify predictors of response to fluvoxamine. The accuracy and weighted kappa were used to evaluate these models. Results: Our result showed that rs3780413 (mean=-0.69, sd=0.39) and cleaning dimension (mean=-0.61, sd=0.20) had reverse effects on response to fluvoxamine therapy in Bayesian ordinal probit and logistic regression models. In the 75th quantile regression model, marital status (mean=1.62, sd=0.47) and family history (mean=1.33, sd=0.61) had a direct effect, and cleaning (mean=-1.10, sd=0.37) and somatic (mean=-0.58, sd=0.27) dimensions had reverse effects on response to fluvoxamine therapy. Conclusion: Response to fluvoxamine is a multifactorial problem and can be different in the levels of socio-demographic, genetic, and clinical predictors. Marital status, familial history, cleaning, and somatic dimensions are associated with response to fluvoxamine therapy.

scholarly journals Symmetry Dimension in Obsessive–Compulsive Disorder: Prevalence, Severity and Clinical Correlates

2021 ◽  
Vol 10 (2) ◽  
pp. 274
Author(s):  
Aline P. Vellozo ◽  
Leonardo F. Fontenelle ◽  
Ricardo C. Torresan ◽  
Roseli G. Shavitt ◽  
Ygor A. Ferrão ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Obsessive Compulsive Disorder ◽  
Clinical Sample ◽  
Cross Sectional Study ◽  
Obsessive Compulsive ◽  
Chi Square ◽  
Cross Sectional ◽  
Compulsive Disorder ◽  
Clinical Correlates ◽  
Axis I

Background: Obsessive–compulsive disorder (OCD) is a very heterogeneous condition that frequently includes symptoms of the “symmetry dimension” (i.e., obsessions and/or compulsions of symmetry, ordering, repetition, and counting), along with aggressive, sexual/religious, contamination/cleaning, and hoarding dimensions. Methods: This cross-sectional study aimed to investigate the prevalence, severity, and demographic and clinical correlates of the symmetry dimension among 1001 outpatients from the Brazilian Research Consortium on Obsessive–Compulsive Spectrum Disorders. The main assessment instruments used were the Dimensional Yale–Brown Obsessive–Compulsive Scale, the Yale–Brown Obsessive–Compulsive Scale, the USP-Sensory Phenomena Scale, the Beck Depression and Anxiety Inventories, the Brown Assessment of Beliefs Scale, and the Structured Clinical Interview for DSM-IV Axis I Disorders. Chi-square tests, Fisher’s exact tests, Student’s t-tests, and Mann–Whitney tests were used in the bivariate analyses to compare patients with and without symptoms of the symmetry dimension. Odds ratios (ORs) with confidence intervals and Cohen’s D were also calculated as effect size measures. Finally, a logistic regression was performed to control for confounders. Results: The symmetry dimension was highly prevalent (86.8%) in this large clinical sample and, in the logistic regression, it remained associated with earlier onset of obsessive–compulsive symptoms, insidious onset of compulsions, more severe depressive symptoms, and presence of sensory phenomena. Conclusions: A deeper knowledge about specific OCD dimensions is essential for a better understanding and management of this complex and multifaceted disorder.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

scholarly journals Prediction of independence in bowel function after spinal cord injury: validation of a logistic regression model

Spinal Cord ◽  
2020 ◽  
Author(s):  
Omar Khan ◽  
Jetan H. Badhiwala ◽  
Michael G. Fehlings
Keyword(s):  
Spinal Cord ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Clinical Utility ◽  
Logistic Regression Model ◽  
Bowel Function ◽  
Motor Score ◽  
Logistic Regression Models ◽  
Cord Injury

Abstract Study design Retrospective analysis of prospectively collected data. Objectives Recently, logistic regression models were developed to predict independence in bowel function 1 year after spinal cord injury (SCI) on a multicenter European SCI (EMSCI) dataset. Here, we evaluated the external validity of these models against a prospectively accrued North American SCI dataset. Setting Twenty-five SCI centers in the United States and Canada. Methods Two logistic regression models developed by the EMSCI group were applied to data for 277 patients derived from three prospective multicenter SCI studies based in North America. External validation was evaluated for both models by assessing their discrimination, calibration, and clinical utility. Discrimination and calibration were assessed using ROC curves and calibration curves, respectively, while clinical utility was assessed using decision curve analysis. Results The simplified logistic regression model, which used baseline total motor score as the predictor, demonstrated the best performance, with an area under the ROC curve of 0.869 (95% confidence interval: 0.826–0.911), a sensitivity of 75.5%, and a specificity of 88.5%. Moreover, the model was well calibrated across the full range of observed probabilities and displayed superior clinical benefit on the decision curve. Conclusions A logistic regression model using baseline total motor score as a predictor of independent bowel function 1 year after SCI was successfully validated against an external dataset. These findings provide evidence supporting the use of this model to enhance the care for individuals with SCI.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

The Structure, Validity, and Specificity of OCD Symptoms

2017 ◽  
pp. 232-259
Author(s):  
David Watson ◽  
Michael W. O’Hara
Keyword(s):  
Logistic Regression ◽  
Scale Development ◽  
Obsessive Compulsive Disorder ◽  
Criterion Validity ◽  
Incremental Validity ◽  
Regression Analyses ◽  
Diagnostic Specificity ◽  
Obsessive Compulsive ◽  
Symptom Dimensions ◽  
Compulsive Disorder

This chapter reviews early attempts to assess obsessive-compulsive disorder (OCD) symptoms, as well as the structural evidence that eventually led to the identification of core, consensual dimensions within the disorder. It then reviews instruments (the Obsessive-Compulsive Inventory; the Schedule of Compulsions, Obsessions, and Pathological Impulses; and the Yale-Brown Obsessive-Compulsive Scale) that have been developed to assess these core symptom dimensions and discusses the validity (including convergent, discriminant, criterion, and incremental validity) and specificity of the scales included in these measures. Three sets of OCD-related items (representing Cleaning, Checking, and Ordering) marked clear, replicable factors in the IDAS-II scale development samples. Indicators of checking and ordering/rituals produced the most impressive results overall, exhibiting the strongest criterion validity, good diagnostic specificity, and significant incremental validity in logistic regression analyses. Washing/cleaning symptoms also showed good diagnostic specificity, but they displayed more moderate criterion validity.

Predicting Mortality after Coronary Artery Bypass Surgery

1998 ◽  
Vol 18 (2) ◽  
pp. 229-235 ◽  
Author(s):  
◽  
Jack V. Tu ◽  
Milton C. Weinstein ◽  
Barbara J. McNeil ◽  
C. David Naylor
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Regression Models ◽  
Artery Bypass ◽  
Training Set ◽  
Cabg Surgery ◽  
Logistic Regression Models ◽  
Test Sets

Objective. To compare the abilities of artificial neural network and logistic regression models to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery. Methods. Neural network and logistic regression models were developed using a training set of 4,782 patients undergoing CABG surgery in Ontario, Canada, in 1991, and they were validated in two test sets of 5,309 and 5,517 patients having CABG surgery in 1992 and 1993, respectively. Results. The probabilities predicted from a fully trained neural network were similar to those of a “saturated” regression model, with both models detecting all possible interactions in the training set and validating poorly in the two test sets. A second neural network was developed by cross-validating a network against a new set of data and terminating network training early to create a more generalizable model. A simple “main effects” regression model without any interaction terms was also developed. Both of these models validated well, with areas under the receiver operating characteristic curves of 0.78 and 0.77 (p > 0.10) in the 1993 test set. The predictions from the two models were very highly correlated (r = 0.95). Conclusions. Artificial neural networks and logistic regression models learn similar relationships between patient characteristics and mortality after CABG surgery.

Bootstrap internal validation command for predictive logistic regression models

2021 ◽  
Vol 21 (2) ◽  
pp. 498-509
Author(s):  
B. M. Fernandez-Felix ◽  
E. García-Esquinas ◽  
A. Muriel ◽  
A. Royuela ◽  
J. Zamora
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Predictive Model ◽  
Performance Measures ◽  
Predictive Models ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Model Performance ◽  
Internal Validation ◽  
Logistic Regression Models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.

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