Relevant bibliographies by topics / Logistic regression analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Logistic regression analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Logistic regression analysis"

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Jessen, Hans Christian, and S. Menard. "Applied Logistic Regression Analysis." Statistician 45, no. 4 (1996): 534. http://dx.doi.org/10.2307/2988559.

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Kilic, Selim. "Binary logistic regression analysis." Journal of Mood Disorders 5, no. 4 (2015): 191. http://dx.doi.org/10.5455/jmood.20151202122141.

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Ziegel, Eric R., and Scott Menard. "Applied Logistic Regression Analysis." Technometrics 38, no. 2 (May 1996): 192. http://dx.doi.org/10.2307/1270433.

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YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (December 2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.
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Tripepi, G., K. J. Jager, F. W. Dekker, and C. Zoccali. "Linear and logistic regression analysis." Kidney International 73, no. 7 (April 2008): 806–10. http://dx.doi.org/10.1038/sj.ki.5002787.

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ABBOTT, ROBERT D. "LOGISTIC REGRESSION IN SURVIVAL ANALYSIS." American Journal of Epidemiology 121, no. 3 (March 1985): 465–71. http://dx.doi.org/10.1093/oxfordjournals.aje.a114019.

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Shott, S. "Logistic regression and discriminant analysis." Journal of the American Veterinary Medical Association 198, no. 11 (June 1, 1991): 1902–5. http://dx.doi.org/10.2460/javma.1991.198.11.1902.

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Wu, Ju. "Study Applicable for Multi-Linear Regression Analysis and Logistic Regression Analysis." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 782–86. http://dx.doi.org/10.2174/1874129001408010782.

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Current study focus on using method of multi-linear regression analysis and logistic regression analysis, and discuss about the condition and scope of multi-linear regression analysis and logistic regression analysis. A modeling method has been introduced keeping in the basic principles of multi-linear regression analysis and logistic regression analysis. The modeling method and two forms of analytic methods have been analyzed, based on two clinic test data of diabetes and Model-2 diabetes as objects of study in combination with the analytic methods of multi-linear regression and logistic regression. Analysis result indicate that glycosylated hemoglobin, glycerin trilaurate, total cholesterol of serum and blood sugar concentration present obvious positive relation (P < 0.05), whereas insulin and blood sugar present negative relation(P < 0.05); body mass index (BMI) and relative factors are dangerous; physical excise and relative factors are protective. In conclusion, multi-linear regression analysis and logistic regression analysis respectively have their own emphasis; for example, multi-linear regression analysis emphasizes on analyzing linear dependent relation with an dependent variable and multiple independent variables, whereas logistic regression analysis emphasizes on analyzing the relation between probability of occurring an incident and independent variables.
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Guns, M., and V. Vanacker. "Logistic regression applied to natural hazards: rare event logistic regression with replications." Natural Hazards and Earth System Sciences 12, no. 6 (June 18, 2012): 1937–47. http://dx.doi.org/10.5194/nhess-12-1937-2012.

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Abstract. Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
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Steyerberg, Ewout W., Marinus J. C. Eijkemans, Frank E. Harrell, and J. Dik F. Habbema. "Prognostic Modeling with Logistic Regression Analysis." Medical Decision Making 21, no. 1 (February 2001): 45–56. http://dx.doi.org/10.1177/0272989x0102100106.

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Dissertations / Theses on the topic "Logistic regression analysis"

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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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Olsén, Johan. "Logistic regression modelling for STHR analysis." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148971.

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Coronary artery heart disease (CAD) is a common condition which can impair the quality of life and lead to cardiac infarctions. Traditional criteria during exercise tests are good but far from perfect. A lot of patients with inconclusive tests are referred to radiological examinations. By finding better evaluation criteria during the exercise test we can save a lot of money and let the patients avoid unnecessary examinations. Computers record amounts of numerical data during the exercise test. In this retrospective study 267 patients with inconclusive exercise test and performed radiological examinations were included. The purpose was to use clinical considerations as-well as mathematical statistics to be able to find new diagnostic criteria. We created a few new parameters and evaluated them together with previously used parameters. For women we found some interesting univariable results where new parameters discriminated better than the formerly used. However, the number of females with observed CAD was small (14) which made it impossible to obtain strong significance. For men we computed a multivariable model, using logistic regression, which discriminates way better than the traditional parameters for these patients. The area under the ROC curve was 0:90 (95 % CI: 0.83-0.97) which is excellent to outstanding discrimination in a group initially included due to their inconclusive results. If the model can be proved to hold for another population it could contribute a lot to the diagnostics of this common medical conditions
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Hu, ChungLynn. "Nonignorable nonresponse in the logistic regression analysis /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.

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Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able to determine the risk profile. The analysis is based on survey data from respondents living in Sweden. The main findings are that variables such as income, consumption rate, experience of a financial bear market, and various psychometric variables are significant in determining a financial risk profile.
I samband med en ökad automatiseringstrend har digital investeringsrådgivning dykt upp som ett nytt fenomen. Av central betydelse är tjänstens förmåga att bedöma en investerares förmåga till att bära finansiell risk. Logistik regression tillämpas för att bedöma en icke- professionell investerares vilja att bära finansiell risk. Målet med uppsatsen är således att identifiera ett antal faktorer med signifikant förmåga till att bedöma en icke-professionell investerares riskprofil. Med andra ord, så syftar denna uppsats till att studera förmågan hos ett antal socioekonomiska- och psykometriska variabler. För att därigenom utveckla en prediktiv modell som kan skatta en individs finansiella riskprofil. Analysen genomförs med hjälp av en enkätstudie hos respondenter bosatta i Sverige. Den huvudsakliga slutsatsen är att en individs inkomst, konsumtionstakt, tidigare erfarenheter av abnorma marknadsförhållanden, och diverse psykometriska komponenter besitter en betydande förmåga till att avgöra en individs finansiella risktolerans
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Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.

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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
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Pan, Tianshu. "Using the multivariate multilevel logistic regression model to detect DIF a comparison with HGLM and logistic regression DIF detection methods /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008.
Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.
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McGlothlin, Anna E. Stamey James D. Seaman John Weldon. "Logistic regression with misclassified response and covariate measurement error a Bayesian approach /." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5101.

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Lindroth, Henriksson Amelia, and Simon Koller. "Logistic Regression Analysis of Patent Approval Rate in Sweden." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230143.

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This thesis was conducted to investigate what factors impact the outcome of a patent application for the Swedish market. The method used was logistic regression and the data was extracted from the database of The Swedish Patent and Registration Offi ce, PRV. The analysis in this thesis started with 47 covariates, including the 35 IPO technical fields, resulting in a model consisting of five covariates. The most important covariates were determined to be the number of notices issued by PRV, whether or not a patent attorney was used and applicant type. The number of notices had a positive impact on the probability of the success of a patent application. Being a company and hiring a patent attorney also increase the chances of the patent being granted. The derived final model showed a high predictive ability and provides insight of significant factors of a successful patent application.
Denna avhandling utfördes för att undersöka vilka faktorer som påverkar utfallen av patentansökningar för den svenska marknaden. Metoden som användes var logistisk re- gression, och datan är hämtad från Patent- och Registreringsverkets, PRVs, databas. Analysen i avhandlingen utfördes på 47 kovariat, inklusive IPOs 35 teknikområden. Detta resulterade i en modell som består av fem kovariat. De viktigaste kovariaten beräknades vara antalet skick mellan PRV och sökanden, huruvida man nyttjat sig av ett patentombud eller ej samt om sökande var en privatperson eller juridisk person. Antalet skick hade en positiv påverkan på sannolikheten för en godkänd patentansökan. Företag och sökanden som använde sig av ett patentombud hade också högre sannolikhet att få sina patent godkända. Den härledda slutgiltiga modellen visade sig ha hög förutsägningsförmåga och ger en insikt om signifikanta faktorer för en framgångsrik patentansökan.
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Heise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.

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In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index. Most experimenters will be less familiar with the Gumbel bivariate logistic regression model than with the univariate logistic regression models which comprise its marginals. Therefore, the optimal designs based on the Gumbel model are evaluated based on univariate logistic regression D-efficiencies; conversely, designs derived from the univariate logistic regression model are evaluated with respect to the Gumbel optimality criteria. Further practical considerations motivate an exploration of designs providing a maximum compromise between the three Gumbel-based criteria D, Q and T. Finally, 5-point designs which can be generated by fitted equations are proposed as a practical option for experimental use.
Ph. D.
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Jin, Yi. "Regression Analysis of University Giving Data." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/1.

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This project analyzed the giving data of Worcester Polytechnic Institute's alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due to the nature of the data, multiple imputation was performed on the missing information to validate generalization of the models to a broader population. Concepts from the field of direct and database marketing, like "score" and "lift", were also introduced in this report.
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Books on the topic "Logistic regression analysis"

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Menard, Scott W. Logistic regression. Thousand Oaks, Calif: Sage Publications, 2009.

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Menard, Scott. Applied Logistic Regression Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412983433.

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Hilbe, Joseph. Logistic regression models. Boca Raton: Chapman & Hall/CRC, 2009.

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Hosmer, David W. Applied Logistic Regression. New York: John Wiley & Sons, Ltd., 2004.

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Stanley, Lemeshow, ed. Applied logistic regression. New York: Wiley, 1989.

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Stanley, Lemeshow, ed. Applied logistic regression. 2nd ed. New York: Wiley, 2000.

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Institute, SAS, ed. Logistic regression examples using the SAS system. 6th ed. Cary, NC: SAS Institute, 1995.

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Mitchel, Klein, and Pryor Erica Rihl, eds. Logistic regression: A self-learning text. 2nd ed. New York: Springer, 2002.

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Nakache, Jean-Pierre. Statistique explicative appliquée: Analyse discriminante, modèle logistique, segmentation par arbre. Paris: Technip, 2003.

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Vach, Werner. Logistic regression with missing values in the covariates. New York: Springer-Verlag, 1994.

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Book chapters on the topic "Logistic regression analysis"

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Judd, Charles M., Gary H. McClelland, and Carey S. Ryan. "Logistic Regression." In Data Analysis, 339–53. Third Edition. | New York : Routledge, 2017. | Revised edition: Routledge, 2017. http://dx.doi.org/10.4324/9781315744131-14.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Logistic Regression." In Multivariate Analysis, 267–354. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_5.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Logistic Regression." In Multivariate Analysis, 265–352. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_5.

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Afifi, A. A., and V. Clark. "Logistic regression." In Computer-Aided Multivariate Analysis, 281–305. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3342-3_12.

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Hartmann, Florian G., Johannes Kopp, and Daniel Lois. "Logistic Regression." In Social Science Data Analysis, 141–63. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41230-2_8.

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Heiberger, Richard M., and Burt Holland. "Logistic Regression." In Statistical Analysis and Data Display, 593–629. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_17.

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Heiberger, Richard M., and Burt Holland. "Logistic Regression." In Statistical Analysis and Data Display, 527–63. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_17.

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Kleinbaum, David G. "Analysis of Matched Data Using Logistic Regression." In Logistic Regression, 227–51. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_8.

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Heumann, Christian, Michael Schomaker, and Shalabh. "Logistic Regression." In Introduction to Statistics and Data Analysis, 315–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11833-3_12.

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Nayebi, Hooshang. "Logistic Regression Analysis." In Advanced Statistics for Testing Assumed Casual Relationships, 79–109. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54754-7_3.

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Conference papers on the topic "Logistic regression analysis"

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van Erp, N., and P. van Gelder. "Bayesian logistic regression analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819994.

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Duller, Christine. "Model selection for logistic regression models." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756152.

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Ramadhan, W. P., S. T. M. T. Astri Novianty, and S. T. M. T. Casi Setianingsih. "Sentiment analysis using multinomial logistic regression." In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC). IEEE, 2017. http://dx.doi.org/10.1109/iccerec.2017.8226700.

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Bhattacharyya, Sidhakam, and Gautam Bandyopadhyay. "Comparative analysis using multinomial logistic regression." In 2014 2nd International Conference on Business and Information Management (ICBIM). IEEE, 2014. http://dx.doi.org/10.1109/icbim.2014.6970970.

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Sastry, Anita S., Vidya U K, Srusthti, and Harshitha T. "Parkinson's Disease Analysis Using Logistic Regression." In 2023 International Conference on Computational Intelligence for Information, Security and Communication Applications (CIISCA). IEEE, 2023. http://dx.doi.org/10.1109/ciisca59740.2023.00070.

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Kumar, S., N. Kaur, Kavita, and A. Joshi. "Tweet sentiment analysis using logistic regression." In International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023). Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/icp.2023.1801.

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Araveeporn, Autcha, and Choojai Kuharatanachai. "Comparing Penalized Regression Analysis of Logistic Regression Model with Multicollinearity." In the 2019 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3343485.3343487.

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Xu, Easton Li, Xiaoning Qian, Tie Liu, and Shuguang Cui. "Pairwise interaction analysis of logistic regression models." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905829.

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Intha, Jakrith, Part Pramokchon, Paween Khoenkaw, and Kitisak Osathanunkul. "Analysis of Effective Sleep through Logistic Regression." In 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE, 2020. http://dx.doi.org/10.1109/ectidamtncon48261.2020.9090702.

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Zou, Xiaonan, Yong Hu, Zhewen Tian, and Kaiyuan Shen. "Logistic Regression Model Optimization and Case Analysis." In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962457.

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Reports on the topic "Logistic regression analysis"

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Fraser, R., R. Fernandes, and R. Latifovic. Multi-temporal Burned area Mapping Using Logistic Regression Analysis and Change Metrics. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219870.

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Arhin, Stephen, Babin Manandhar, and Adam Gatiba. Influence of Pavement Conditions on Commercial Motor Vehicle Crashes. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2343.

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Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary logistic regression to explore relationships between injury occurrence after a CMV crash and multiple independent variables. Additionally, Artificial Neural Network (ANN) models were developed to classify CMV crash injury severity. Importantly, the inclusion of pavement condition variables (International Roughness Index and Pavement Condition Index) substantially enhanced the accuracy of the logistic regression model, increasing predictability from 0.8% to 41%. The study also demonstrates the potential of Artificial Neural Network models in predicting CMV crash injury severity, achieving an accuracy of 60% and an F-measure of 0.52. These results highlight the importance of considering road pavement conditions in road safety policies and interventions. The study provides valuable insights for policymakers and stakeholders aiming to enhance road safety for CMVs in the District of Columbia and showcases the potential of machine learning techniques in understanding the complex interplay between road conditions and CMV crash occurrences.
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Chierichetti, Maria, Armin Chierichetti, and Fatemeh Davoudi. Design of an Evaluation Plan for Senate Bill 1046. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2021.2209.

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In an effort to understand and decrease alcohol-impaired driving as a primary collision factor In California, the research team designed an evaluation plan for California Senate Bill 1046 and its focus on ignition interlock devices as a sentence for Driving Under Influence offense. This plan will evaluate whether Senate Bill 1046 affected the Driving Under the Influence crash frequency and severity, and whether sociodemographic and geographic factors influence its effectiveness. This report lays the foundation for the evaluation that will be conducted in 2024. The research team conducted a meta-analysis of the last 12 years of literature and research on ignition interlock programs inside and outside the United States. Based on the findings of this analysis, the recommended evaluation plan of the law revolves around three research questions that focus on the changes in the frequency/severity of DUI-related crashes in California, the impact of the law on recidivism and on interlock installation rates. To respond to these questions, the research team recommends a list of data that should be collected, such as the number of injuries and deaths resulting from alcohol-related motor vehicle accidents, installation rates of ignition interlocks compared to the prior five-year period, the number of individuals who were required to have an ignition interlock device installed who were convicted of an alcohol-related violation, as well as number of lockouts while an interlock is installed. The research team proposed several statistical approaches for the analysis of this data, such as descriptive statistics, time series analysis, analysis of variance, and logistic regression.
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Chierichetti, Maria, Armin Chierichetti, and Fatemeh Davoudi. Design of an Evaluation Plan for Senate Bill 1046. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2022.2209.

Full text
Abstract:
In an effort to understand and decrease alcohol-impaired driving as a primary collision factor In California, the research team designed an evaluation plan for California Senate Bill 1046 and its focus on ignition interlock devices as a sentence for Driving Under Influence offense. This plan will evaluate whether Senate Bill 1046 affected the Driving Under the Influence crash frequency and severity, and whether sociodemographic and geographic factors influence its effectiveness. This report lays the foundation for the evaluation that will be conducted in 2024. The research team conducted a meta-analysis of the last 12 years of literature and research on ignition interlock programs inside and outside the United States. Based on the findings of this analysis, the recommended evaluation plan of the law revolves around three research questions that focus on the changes in the frequency/severity of DUI-related crashes in California, the impact of the law on recidivism and on interlock installation rates. To respond to these questions, the research team recommends a list of data that should be collected, such as the number of injuries and deaths resulting from alcohol-related motor vehicle accidents, installation rates of ignition interlocks compared to the prior five-year period, the number of individuals who were required to have an ignition interlock device installed who were convicted of an alcohol-related violation, as well as number of lockouts while an interlock is installed. The research team proposed several statistical approaches for the analysis of this data, such as descriptive statistics, time series analysis, analysis of variance, and logistic regression.
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Dütsch, Matthias, and Ralf Himmelreicher. Characteristics contributing to low- and minimum-wage labour in Germany. Otto-Friedrich-Universität, 2022. http://dx.doi.org/10.20378/irb-54129.

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In this article we examine the characteristics of individuals, companies, and industries involved in low-wage labour in Germany to understand their impact on the risks workers face of earning hourly wages that are below the minimum-wage and low-wage thresholds. To identify these characteristics, we use the Structure of Earnings Survey 2014 (SES). The SES is a mandatory survey of companies which provides information on wages and working hours from about 1 million jobs and nearly 70,000 compa-nies from all industries. This data allows us to present the first systematic analysis of the interaction of individual-, company-, and industry-level factors on minimum- and low-wage working in Germany. Using a descriptive analysis, we first give an overview of typical low-paying jobs, companies, and in-dustries. Second, we use random intercept-only models to estimate the explanatory power of the indi-vidual, company, and industry levels. One main finding is that the influence of individual characteristics on wage levels is often overstated: Less than 25 percent of the differences in the employment situa-tion regarding being employed in minimum-wage or low-wage jobs can be attributed to the individual level. Third, we performed logistic and linear regression estimations to assess the risks of having a minimum- or low-wage job and the distance between a worker’s actual earnings and the minimum- and low-wage thresholds. Our findings allow us to conclude that several determinants related to indi-viduals appear to suggest a high low-wage incidence, but in fact lose their explanatory power once controls are added for factors relating to the companies or industries that employ these individuals.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, December 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Lubowa, Nasser, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Pharmaceutical Industry in Uganda: A Review of the Common GMP Non-conformances during Regulatory Inspections. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317442.

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The prevalence of substandard medicines in Africa is high but not well documented. Low and Middle-Income Countries (LMICs) are likely to face considerable challenges with substandard medications. Africa faces inadequate drug regulatory practices, and in general, compliance with Good Manufacturing Practices (GMP) in most of the pharmaceutical industries is lacking. The majority of pharmaceutical manufacturers in developing countries are often overwhelmed by the GMP requirements and therefore are unable to operate in line with internationally acceptable standards. Non-conformances observed during regulatory inspections provide the status of the compliance to GMP requirements. The study aimed to identify the GMP non-conformances during regulatory inspections and gaps in the production of pharmaceuticals locally manufactured in Uganda by review of the available 50 GMP reports of 21 local pharmaceutical companies in Uganda from 2016. The binary logistic generalized estimating equations (GEE) model was applied to estimate the association between odds of a company failing to comply with the GMP requirements and non-conformances under each GMP inspection parameter. Analysis using dummy estimation to linear regression included determination of the relationship that existed between the selected variables (GMP inspection parameters) and the production capacity of the local pharmaceutical industry. Oral liquids, external liquid preparations, powders, creams, and ointments were the main categories of products manufactured locally. The results indicated that 86% of the non-conformances were major, 11% were minor, and 3% critical. The majority of the non-conformances were related to production (30.1%), documentation (24.5%), and quality control (17.6%). Regression results indicated that for every non-conformance under premises, equipment, and utilities, there was a 7-fold likelihood of the manufacturer failing to comply with the GMP standards (aOR=6.81, P=0.001). The results showed that major non-conformances were significantly higher in industries of small scale (B=6.77, P=0.02) and medium scale (B=8.40, P=0.04), as compared to those of large scale. This study highlights the failures in quality assurance systems and stagnated GMP improvements in these industries that need to be addressed by the manufacturers with support from the regulator. The addition of risk assessment to critical production and quality control operations and establishment of appropriate corrective and preventive actions as part of quality management systems are required to ensure that quality pharmaceuticals are manufactured locally.
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Lifshitz, Guy, Rotem Franko, Nitzan Goldberg, Nili Gutwetter, Nayyera Tibi, Shmuel Avital, and Yaron Rudnicki. Can Conservative Management of Simple Acute Appendicitis Miss a Diagnosis of Appendiceal Neoplasms? A Histological Evaluation of 686 Appendectomies of Simple Appendicitis. International Journal of Surgery, June 2024. http://dx.doi.org/10.60122/j.ijs.2024.20.02.

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Background: Acute appendicitis is one of the most common surgical disease with an estimated lifetime risk of about 6-8%. Non-operative management of appendicitis is gaining popularity worldwide. Appendiceal tumors are rare and confirmed by histopathological examination in 0.5-2.5% of all appendectomies. The risk of missing an appendiceal tumor with a non-operative treatment is not well established. The aim of this study was to assess the incidence of appendiceal neoplasm in patients presented with simple non-perforated appendicitis. Materials and Methods: A retrospective study of all patients, that underwent an appendectomy from January 2018 to June 2020 in a single academic center. The histopathological reports were reviewed for appendiceal tumor. The patients’ and disease characteristics were recorded. Final analysis included only patients with simple acute appendicitis. Results: 686 patients that underwent an emergent appendectomy for a simple acute appendicitis, nine patients (1.41%) were found to have an appendiceal neoplasm on final pathology. The preoperative imaging study did not reveal any suspicious findings for appendiceal neoplasm. Neoplasms revealed by histopathological examination include three neuroendocrine tumor (NET), four low-grade mucinous neoplasm and two adenocarcinomas. The mean age for appendiceal NET was 25.33 ± 4.72, for mucinous neoplasm 48.75 ± 29.22 and 62 ± 12.72 for adenocarcinoma. Logistic regression demonstrated a significant difference in appendiceal diameter and white blood cell count between the neoplasm and acute appendicitis group. Conclusion: Although appendiceal neoplasm following an appendectomy for simple acute appendicitis is not common, it is a concern that need to be addressed. Despite the wide use of imaging study for the diagnosis, it does not provide diagnostic indication for the existence of an appendiceal neoplasm in our cases. The risk of an appendiceal neoplasm, albeit low, should be taken into consideration in the management of adult patients with acute appendicitis before a decision to embark on a non-operative therapy.
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Haubrich, Julia, Sarah Benz, Ullrich Isermann, Beat Schäffer, Rainer Schmid, Dirk Schreckenberg, Jean Marc Wunderli, and Rainer Guski. Leq+X - Lärmexposition, Ereignishäufigkeiten und Belästigung: Re-Analyse von Daten zur Belästigung und Schlafstörung durch Fluglärm an deutschen und Schweizer Flughäfen. Universitätsbibliothek der Ruhr-Universität Bochum, 2020. http://dx.doi.org/10.46586/rub.164.139.

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In this study, part of the data sets from 4 large Swiss and German aircraft noise impact studies are re-analysed using logistic multi-level regression models. The aim is to investigate the assumptions that the prediction of a) the percentage of persons highly annoyed by aircraft noise or b) the percentage of persons highly sleep disturbed by aircraft noise can be improved if (i) instead of the energy-equivalent continuous noise level alone, either additional or alternative, more frequency-based aircraft noise metrics and (ii) also airport-specific characteristics are used as predictors. The results support both assumptions; both regarding the percentage of persons highly annoyed and regarding the percentage of persons highly sleep disturbed.
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