Open AccessBook
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TLDR
Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.Abstract:
From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models... Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."- Choice "Well written, clearly organized, and comprehensive... the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." - Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.read more
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Journal ArticleDOI
AUC: a misleading measure of the performance of predictive distribution models
TL;DR: The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models as discussed by the authors.
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Evaluating non-randomised intervention studies.
Jonathan J Deeks,Jacqueline Dinnes,Roberto D'Amico,Amanda Sowden,C Sakarovitch,Fujian Song,Mark Petticrew,Douglas G. Altman +7 more
TL;DR: The inability of case-mix adjustment methods to compensate for selection bias and the inability to identify non- randomised studies that are free of selection bias indicate that non-randomised studies should only be undertaken when RCTs are infeasible or unethical.
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Twelve-Month Use of Mental Health Services in the United States Results From the National Comorbidity Survey Replication
Philip S. Wang,Michael Lane,Mark Olfson,Harold Alan Pincus,Kenneth B. Wells,Ronald C. Kessler +5 more
TL;DR: Most people with mental disorders in the United States remain either untreated or poorly treated, and interventions are needed to enhance treatment initiation and quality.
Journal ArticleDOI
Purposeful selection of variables in logistic regression
TL;DR: An algorithm which automates the purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process and has the capability of retaining important confounding variables, resulting potentially in a slightly richer model.
Journal ArticleDOI
Complications of Endoscopic Biliary Sphincterotomy
Martin L. Freeman,Douglas B. Nelson,Stuart Sherman,Haber Gb,Herman Me,P. J. Dorsher,J. P. Moore,Fennerty Mb,Michael E. Ryan,Michael J Shaw,Lande Jd,A. M. Pheley +11 more
TL;DR: The rate of complications after endoscopic biliary sphincterotomy can vary widely in different circumstances and is primarily related to the indication for the procedure and to endoscopic technique, rather than to the age or general medical condition of the patients.