Use Of Logistic Regression Research Articles

Estimating the Likelihood of Wireless Coexistence Using Logistic Regression: Emphasis on Medical Devices.

Medical device manufacturers incorporate wireless technology in their designs to offer convenience and agility to both patients and caregivers. However, the use of unlicensed radio spectrum bands by both medical devices and other equipment raises concerns about wireless coexistence. Work by the accredited standards committee C63 of the American National Standards Institute (ANSI) to provide the community with a consensus standard for coexistence evaluation resulted in the publication of the ANSI C63.27 standard, which was later recognized by the U.S. Food and Drug Administration. Estimating the likelihood of wireless coexistence of a system under test (SUT) in a given environment is central to the evaluation and reporting of wireless coexistence, as made clear in the C63.27 standard. However, no method to perform this estimation is provided. In this paper, we propose the use of logistic regression (LR) to estimate the likelihood of wireless coexistence of a medical device in its intended environment. Radiated open environment coexistence testing was used to realize a test scenario in which the interfering network was IEEE 802.11n Wi-Fi and the SUT was ZigBee; exemplary wireless technologies for interfering network and medical device, respectively. LR model fitting was then performed to derive a model that describes the performance of SUT under a range of wireless coexistence phenomena. Finally, results were incorporated with the outcome of a spectrum survey using Monte Carlo simulation to estimate the SUT likelihood of wireless coexistence in a hospital environment.

A preoperative risk score to predict red blood cell transfusion in patients undergoing hysterectomy for ovarian cancer

Patients with ovarian cancer experience a high rate of anemia throughout their treatment course, with rates that range from 19-95%. Blood transfusions offer symptom relief but may be costly, are limited in supply, and have been associated with worse 30-day surgical morbidity and mortality rates. The purpose of this study was to identify risk factors for blood transfusion with packed red blood cell and to develop a transfusion risk score to identify patients who undergo surgery for ovarian cancer and who are at lowest risk for a blood transfusion. Our aim was to help clinicians identify those patients who may not require a crossmatch to encourage resource use and cost-savings. This is a retrospective database cohort study of 3470 patients who underwent hysterectomy for ovarian cancer with the use the National Surgical Quality Improvement Program database from 2014-2016. The association between risk factors with respect to 30-day postoperative blood transfusion was modeled with the use of logistic regression. A risk score to predict blood transfusion was created. Eight hundred ninety-one (25.7%) patients received a blood transfusion. In multivariate analysis, blood transfusion was associated independently with age (odds ratio, 1.90, P<.01), African American race (odds ratio, 2.30; P<.01), ascites (odds ratio, 1.89; P=.02), preoperative hematocrit level <30% (odds ratio, 10.70; P<.01), preoperative platelet count >400×109/L (odds ratio, 1.75; P<.01), occurrence of disseminated cancer (odds ratio, 1.71; P<.01), open surgical approach (odds ratio, 7.88; P<.01), operative time >3 hours (odds ratio, 2.19; P<.01), and additional surgical procedures that included large bowel resection (odds ratio, 4.23; P<.01), bladder/ureter resection (odds ratio, 1.69; P=.02), and pelvic exenteration (P=.02). A preoperative risk score that used age, race, ascites, preoperative hematocrit level, platelets, presence of disseminated cancer, planned hysterectomy approach, and procedures accurately predicted blood transfusion with good discriminatory ability (C-statistic=0.80 [P<.001]; C-statistic=0.69 [P<.001] for derivation and validation datasets, respectively) and calibration (Hosmer-Lemeshow goodness-of-fit, P=.081; P=.56 for derivation and validation datasets, respectively). Patients who undergo hysterectomy for ovarian cancer experience a high incidence of blood transfusions in the perioperative period. Preoperative risk factors and planned surgical procedures can be used in our transfusion risk score to help predict anticipated blood requirements.

Moving the Needle on Evaluating Multivariate Screening Accuracy

The assessment of screening accuracy and setting of cut points for a universal screener have traditionally been evaluated using logistic regression analysis. This analytic technique has been frequently used to evaluate the trade-offs in correct classification with misidentification of individuals who are at risk of performing poorly on a later outcome. Although useful statistically, coefficients from a multiple logistic regression can be difficult to explain to practitioners as it pertains to classification decisions. Moreover, classifications based on multivariate assessments are challenging to understand how performance on one assessment compensates for performance on another. The purpose of this article is to demonstrate and compare the use of logistic regression with classification and regression tree (CART) models in the identification of students who are at risk of reading comprehension difficulties. Data consisted of 986 Grade 1 students and 887 Grade 2 students who were administered a screening assessment at the middle of the school year as well as the 10th edition of the Stanford Achievement Test. Results indicated that CART performs comparably with logistic regression and may assist researchers and practitioners in explaining classification rules to parents and educators.

Out-of-home placement in early childhood and psychiatric diagnoses and criminal convictions in young adulthood: a population-based propensity score-matched study

To ensure their protection and healthy development, children exposed to adverse family circumstances might be placed in foster homes, institutions, or kinship care (out-of-home placement). We aimed to compare the rates of psychiatric diagnoses and criminal convictions in young adulthood (ages 18-25 years) among children who were first placed at ages 2-6 years with those of children who were not placed and who had similar sociodemographic and family characteristics. We did a population-wide cohort study using the 1987 Finnish Birth Cohort, which collects longitudinal data linking nationwide child welfare, medical, and criminal registers for all 59 476 livebirths in Finland in 1987. The exposure was the first out-of-home placement at ages 2-6 years. Outcomes were rates of psychiatric diagnoses, criminal convictions, and prescriptions for psychotropic medication filled at ages 18-25 years. We matched cases to non-placed controls using propensity score matching on parental characteristics (eg, age, psychiatric diagnoses, education, family structure) and child characteristics (eg, neurodevelopmental problems, prematurity). Differences in adult outcomes between children placed and matched controls were assessed by use of logistic regression on the matched cohort. Of 54 814 individuals with complete data, 388 (1%) were first placed at ages 2-6 years; matched controls were identified for 386 of these children. At ages 18-25 years, those who had been placed as children had greater odds than never-placed controls of substance-related disorders (odds ratio 2·10, 95% CI 1·27-3·48), psychotic or bipolar disorders (3·98, 1·80-8·80), depression or anxiety (2·15, 1·46-3·18), neurodevelopmental disorders (3·59, 1·17-11·02), or other disorders (2·06, 1·25-3·39). Participants who were placed had more psychotropic medication prescriptions (1·96, 1·38-2·80) and higher rates of criminal convictions (violent offences, 2·43, 1·61-3·68; property offences, 1·86, 1·17-2·97). Preschool children who are placed out-of-home are at risk of adverse outcomes as adults, even accounting for their initial circumstances. It is important to explore which conditions lead to more or less favourable outcomes in child protection. Academy of Finland.

Maternal Lipids at 28 Weeks’ Gestation and Offspring Adiposity at Age 5 to 7 Years

Obesity is a global epidemic, and there is a focus on identifying markers of obesity in children with a view to prevention. We aimed to examine prospectively the association of maternal fasting lipids with adiposity in 5- to 7-year-old offspring in a large observational study. Pregnant women (1612) were recruited to the Belfast center of the Hyperglycemia and Adverse Pregnancy Outcome study in a large tertiary maternity hospital at an average of 28 weeks' gestation. Maternal fasting total cholesterol, low-density lipoprotein-cholesterol, triglycerides, and high-density lipoprotein-cholesterol were estimated at 28 weeks' gestation. Offspring-mother pairs (819) were included in the current study, and adiposity was expressed as body mass index (BMI) z score (1990 British growth standard) and sum of skin-fold (SSF) thicknesses (triceps, subscapular, and suprailiac). Statistical significance was more rigorously defined as P < 0.01 to allow for multiple comparisons. No linear relation was found between maternal lipids and offspring BMI z score or SSFs (P ≥ 0.01) using correlation analysis. With the use of logistic regression, there was no relation between maternal lipids and offspring adiposity controlled for birthweight z score, offspring age, offspring gender, smoking during pregnancy, offspring energy intake and physical activity, maternal BMI during pregnancy, and fasting glucose during pregnancy (P ≥ 0.01). Maternal 28-week gestational fasting lipids are not associated with offspring BMI or subcutaneous adiposity at age 5 to 7 years.

On the use of logistic regression for stellar classification

We are totally immersed in the Big Data era and reliable algorithms and methods for data classification are instrumental for astronomical research. Random Forest and Support Vector Machines algorithms have become popular over the last few years and they are widely used for different stellar classification problems. In this article, we explore an alternative supervised classification method scarcely exploited in astronomy, Logistic Regression, that has been applied successfully in other scientific areas, particularly biostatistics. We have applied this method in order to derive membership probabilities for potential T Tauri star candidates from ultraviolet-infrared colour-colour diagrams.

Social Network Characteristics Are Associated With Type 2 Diabetes Complications: The Maastricht Study.

The relation between clinical complications and social network characteristics in type 2 diabetes mellitus (T2DM) has hardly been studied. Therefore, we examined the associations of social network characteristics with macro- and microvascular complications in T2DM and investigated whether these associations were independent of glycemic control, quality of life, and well-known cardiovascular risk factors. Participants with T2DM originated from the Maastricht Study, a population-based cohort study (n = 797, mean age 62.7 ± 7.6 years, 31% female). Social network characteristics were assessed through a name generator questionnaire. Diabetes status was determined by an oral glucose tolerance test. Macro- and microvascular complications were defined as a history of cardiovascular disease and the presence of impaired vibratory sense and/or retinopathy and/or albuminuria, respectively. We assessed cross-sectional associations of social network characteristics with macro- and microvascular complications by use of logistic regression adjusted for age, HbA1c, quality of life, and cardiovascular risk factors, stratified for sex. A smaller network size, higher percentages of family members, and lower percentages of friends were independently associated with macrovascular complications in both men and women. A smaller network size and less informational support were independently associated with microvascular complications in women, but not in men. This study shows that social network characteristics were associated with macro- and microvascular complications. Health care professionals should be aware of the association of the social network with T2DM outcomes. In the development of strategies to reduce the burden of disease, social network characteristics should be taken into account.

Use of Logistic Regression to Determine Parameter Ability for Recovery Prediction in Experimental Rat Hemorrhagic Shock Model

IntroductionHemorrhagic shock (HS) severely decreases oxygen (O2) delivery and induces cardiovascular collapse. It is of interest for clinicians to estimate the potential of recovery during the early onset of a shock event as that would determine whether resources should be expended in environments of material shortage such as warzones and low resource communities. Multiple parameters, such as mean arterial pressure (MAP) and heart rate, are traditionally monitored during shock as a means of estimating the potential for recovery. In this study we used a logistic regression model to determine the ability of systemic hemodynamics, cardiac function, and blood gas parameters in determining the ability of successful HS recovery.MethodsPacked red blood cells (pRBCs) stored in AS‐3 after leukorfiltration were created from donor Sprague‐Dawley rats. pRBC units were randomly stored under either 1) conventional; 2) anaerobic; or 3) anaerobic/hypercapnic conditions. Rats (150–200g) were hemorrhaged to 50% of blood volume, held in hypovolemia for 30 minutes, and resuscitated to recover blood pressure to 90% pre‐hemorrhage with pRBC stored for either 1 or 3 weeks. Systemic hemodynamic, cardiac function, and blood gas parameters were monitored during shock and resuscitation. The percent resuscitation (%Res) corresponding to the percentage of the animal's original blood volume infused at the end of recovery, was used to create two classes of subjects with %Res=50% (equivalent to approximately 5 blood units) set as the threshold. A binary classification, logistic regression model was created with all measured parameters as features, and trained on random instances. The weights with an above threshold contribution assigned to each parameter after an L2 norm cost function minimization were extracted and matched with the corresponding features creating a reduced feature vector. A Monte Carlo approach was applied for n=100 trials of the previously described procedure to get a distribution of the most commonly selected features. The performance of the new feature vector was assessed by another logistic regression model with a 5‐ fold cross‐validation.ResultsThe top 3 features chosen by the model in order of increasing number of occurrences were: lactate 30 minutes into shock, mean arterial pressure 30 minutes into shock and contractility (dp/dtmax) at baseline. The model trained using these three features had a mean cross‐validation accuracy of 90.2%, significantly above the 66.9% accuracy (p&lt;0.001) for a baseline classifier trained with random features. A final test set accuracy of 88.5% was obtained.ConclusionsIt has been shown that a logistic regression model can be effectively used for the determination of important parameters for the prediction of successful recovery after shock in an experimental rat hemorrhagic shock model.Support or Funding InformationNIH: R01 HL126945, NIH: T32 HL105373This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Childlessness in Twentieth-Century Spain: A Cohort Analysis for Women Born 1920\u20131969

Studies of childlessness in the twentieth century in developed countries have underscored the existence of diverging trends with higher levels among cohorts born at the beginning of the twentieth century, lower ones among the baby boom cohorts and finally higher ones for cohorts born after the Second World War. Spain also shows these basic trends, but the fit is not identical to that of other countries, with differences affecting the timing of trend changes and also the levels of childlessness observed in the final part of the period. This paper focuses on Spanish women born 1920 and 1969 and explores the factors characterizing traditional/old childlessness and how these differ from those holding more recently. Using microdata from Spanish Census of 2011, our approach makes use of logistic regression and regression-based decomposition techniques. Change over time, as measured by inter-cohort variations, reveals strikingly different patterns of behaviour characterized by a reversal of the traditional association of childlessness with marital status and educational attainment that takes place in a period of intense and pervasive social change.

Clinical features, course, and outcomes of a UK cohort of pediatric moyamoya.

To describe characteristics and course of a large UK cohort of children with moyamoya from multiple centers and examine prognostic predictors. Retrospective review of case notes/radiology, with use of logistic regression to explore predictors of outcome. Eighty-eight children (median presentation age 5.1 years) were included. Thirty-six presented with arterial ischemic stroke (AIS) and 29 with TIA. Eighty had bilateral and 8 unilateral carotid circulation disease; 29 patients had posterior circulation involvement. Acute infarction was present in 36/176 hemispheres and chronic infarction in 86/176 hemispheres at the index presentation. Sixty-two of 82 with symptomatic presentation had at least one clinical recurrence. Fifty-five patients were treated surgically, with 37 experiencing fewer recurrences after surgery. Outcome was categorized as good using the Recovery and Recurrence Questionnaire in 39/85 patients. On multivariable analysis, presentation with TIA (odds ratio [OR] 0.09, 95% confidence interval [CI] 0.02-0.35), headache (OR 0.10, 95% CI 0.02-0.58), or no symptoms (OR 0.08, 95% CI 0.01-0.68) was less likely to predict poor outcome than AIS presentation. Posterior circulation involvement predicted poor outcome (OR 4.22, 95% CI 1.23-15.53). Surgical revascularization was not a significant predictor of outcome. Moyamoya is associated with multiple recurrences, progressive arteriopathy, and poor outcome in half of patients, especially with AIS presentation and posterior circulation involvement. Recurrent AIS is rare after surgery. Surgery was not a determinant of overall outcome, likely reflecting surgical case selection and presentation clinical status.

Can Lean and Agile organisations within the UK automotive supply chain be distinguished based upon contextual factors?

PurposeThe purpose of this paper is to robustly establish whether firms are implementing Lean or Agile production in the automotive supply chain (SC) and, by drawing on contingency theory (CT) as our theoretical lens, independently determine whether Lean and Agile firms can be distinguished based upon contextual factors.Design/methodology/approachPrimary quantitative data from 140 firms in the West Midlands (UK) automotive industry were obtained via a constructed survey. Analysis incorporated the use of logistic regressions to calculate the probability of Lean and Agile organisations belonging to different groups amongst the contextual factors investigated.FindingsLean and Agile firms co-exist in the automotive SC and Lean firms were found to be at higher tiers of the SC, while Agile firms were found to be at lower tiers.Originality/valueThe originality of this study lies within the novel methodological attempt used to distinguish Lean and Agile production, based upon the contextual factors investigated. Not only is the importance of CT theoretically approved, but “received wisdom” within SC management is also contested. Extant literature propagates that the automotive SC is comprised of organisations that predominantly adopt Lean production methods, and that in SCs comprised of both Lean and Agile organisations, the firms closer to the customer will adopt more flexible (Agile) practices, while those that operate upstream will adopt more efficient (Lean) practices. The findings from this study have implications for theory and practice, as Lean and Agile firms can be found in the automotive SC without any relationship to the value-adding process. To speculate as to why the findings contest existing views, resource dependence theory and, more specifically, a power perspective, was invoked. The authors provide readers with a new way of thinking concerning complicated SCs and urge that the discipline of SC management adopts a “fourth” SC model, depicting a new Lean and Agile SC configuration.

Associations Between Surgical Ablation and Operative Mortality After Mitral Valve Procedures

Surgical ablation (SA) for atrial fibrillation (AF) concomitant to mitral valve repair/replacement (MVRR) improves longitudinal sinus rhythm. However, the risk of adding SA remains a clinical question. This study examined whether the addition of contemporary SA for AF has an impact on operative outcomes. The study cohort included 88,765 MVRR patients with or without SA, coronary artery bypass grafting (CABG), septal defect, and tricuspid repair in The Society of Thoracic Surgeons Database between 2011 and 2014. Group 1 did not have AF (No-AF) and did not receive SA (No-SA); group 2 had No-AF immediately preoperatively but received SA; group 3 had AF but No-SA; and group 4 had AF with SA. Groups 3 and 4 were stratified into paroxysmal versus nonparoxysmal AF. With the use of logistic regression, with group 1 as reference, risk-adjusted odds ratios (OR) for mortality were compared for SA performance, AF type, and SA technique. Group 3 had higher age, New York Heart Association class, redo operations, and unadjusted mortality than group 4. Relative to group 1, group 3 had an OR for mortality of 1.15 (95% confidence interval: 1.04 to 1.27, p < 0.01). OR increments were similar for paroxysmal and nonparoxysmal AF. In group 4, concomitant SA was independently associated with lower AF-related relative risk (OR 1.08), to a level that was not different from group 1 (p= 0.13). Observed treatment effects were equivalent for paroxysmal and nonparoxysmal AF and across all levels of baseline risk. For patients with AF at the time of mitral operation, the performance of SA seems associated with a lower risk-adjusted operative mortality compared with patients who do not undergo ablation.

Logistic Regression and Response Surface Design for Statistical Modeling of Investment Casting Process in Metal Foam Production

A metal foam represents a promising material since it keeps the high mechanical properties of the metal while reducing the weight up to 90%. Among several manufacturing processes, the investment casting is a foundry process flexible enough to be suitable both for stochastic and for regular foams. This paper presents an experimental determination of the manufacturing process of metal regular foams by investment casting. The goal is to derive experimentally an actual formability map. The use of logistic regression and response surface design is proposed as an effective tool for determining a statistical model of the metal foam casting process.

Responsible Use of Crop Protection Products and Nigeria’s Growth Enhancement Support Scheme

We examine the impact of the federal government of Nigeria’s (FGN) growth enhancement support scheme (GESS) on responsible use of crop protection products (CPPs) in rural Nigeria. Results from the use of logistic regressions show that GESS significantly impact on farmers’ access to CPPs, but does not significantly impact on farmers’ knowledge and skill of CPP application, and in several cases the misuse has led to deterioration of soil fertility. Findings suggests that embracing information on recommended CPPs, dose rates, dilutions, timing, frequency of applications and precautions should form the foundation of GESS activity attributable to CPPs in sub-Saharan Africa.

Use of logistic regression to study the most important factors affecting the incidence of tuberculosis

What distinguishes this research Is to focus on the concept of logistic the regression and its characteristics and how to build the model analytical descriptive approach was used to describe the logistic regression How to estimate Its Features And its use as an important tool in studying the influencing factors in the injury of tuberculosis the sample included 299 patients.

On predicting academic performance with process mining in learning analytics

Purpose The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques. Design/methodology/approach Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used. Findings The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way. Practical implications Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate. Social implications Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course. Originality/value This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

The Logistic Regression from the Viewpoint of the Factor Space Theory

Logistic regression plays an important role in machine learning. People excitingly use it in conceptual matching yet with some details to be understood further. This paper aims to present a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. An example about breast cancer diagnosis is displayed to show how the factor space theory can be incorporated into the understanding and use of logistic regression.

Использование логистической регрессии при выборе способа кардиоплегии

Использование логистической регрессии при выборе способа кардиоплегии

Measurement of faculty anesthesiologists' quality of clinical supervision has greater reliability when controlling for the leniency of the rating anesthesia resident: a retrospective cohort study.

Our department monitors the quality of anesthesiologists' clinical supervision and provides each anesthesiologist with periodic feedback. We hypothesized that greater differentiation among anesthesiologists' supervision scores could be obtained by adjusting for leniency of the rating resident. From July 1, 2013 to December 31, 2015, our department has utilized the de Oliveira Filho unidimensional nine-item supervision scale to assess the quality of clinical supervision provided by faculty as rated by residents. We examined all 13,664 ratings of the 97 anesthesiologists (ratees) by the 65 residents (raters). Testing for internal consistency among answers to questions (large Cronbach's alpha > 0.90) was performed to rule out that one or two questions accounted for leniency. Mixed-effects logistic regression was used to compare ratees while controlling for rater leniency vs using Student t tests without rater leniency. The mean supervision scale score was calculated for each combination of the 65 raters and nine questions. The Cronbach's alpha was very large (0.977). The mean score was calculated for each of the 3,421 observed combinations of resident and anesthesiologist. The logits of the percentage of scores equal to the maximum value of 4.00 were normally distributed (residents, P = 0.24; anesthesiologists, P = 0.50). There were 20/97 anesthesiologists identified as significant outliers (13 with below average supervision scores and seven with better than average) using the mixed-effects logistic regression with rater leniency entered as a fixed effect but not by Student's t test. In contrast, there were three of 97 anesthesiologists identified as outliers (all three above average) using Student's t tests but not by logistic regression with leniency. The 20 vs 3 was significant (P < 0.001). Use of logistic regression with leniency results in greater detection of anesthesiologists with significantly better (or worse) clinical supervision scores than use of Student's t tests (i.e., without adjustment for rater leniency).

TEFL tourism: the tourist who teaches

ABSTRACTThere is evident lineage between the concepts of teaching English as a foreign language (TEFL) and tourism, represented through evocative marketing material, the commoditisation of the TEFL product, teacher motivations and experiences. Yet, to date there has been little attention paid to this relationship. The amalgamation of the two concepts brings rise to the introduction of the niche form of tourism ‘TEFL tourism’, where the tourist travels outside of their usual environment to teach English as a foreign language and whose role shifts between tourist, educator and educatee at various points in their trip. The TEFL tourism phenomenon is explored through the use of a two-phase research approach employing the qualitative examination of blogs written by TEFL teachers and quantitative surveys. Drawing parallels with associated tourism forms including volunteer, education and philanthropic, it is concluded that TEFL tourism is an entity in its own right, with unique characteristics and motivations presented by TEFL tourists. The use of logistic regression facilitated the analysis of TEFL teacher types, presenting a typology classifying tourists as leisure-minded, philanthropy-minded, career-minded and expatriate-minded. This case-study examination facilitates initial comprehension of the TEFL tourism industry, providing basis for subsequent research to be undertaken to enable enhanced sustainable management of the TEFL industry worldwide.