Download or read book Measurement Error in Logistic Regression by Discriminant Analysis with Applications to the Epidemiology of Coronary Heart Disease written by Richard David Bawol and published by . This book was released on 1979 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book American Journal of Public Health written by and published by . This book was released on 1995-07 with total page 1158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1980 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Parameter Estimation for the Logistic Regression Model with Errors in Covariate written by Huyen D. Nguyen and published by . This book was released on 2021 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a logistic regression model, when the covariate is measured with error, the estimators of the regression coefficient parameters can be biased. We propose a method for estimating parameters of a logistic regression with case-control data, when the covariate is subject to measurement error. The density of the covariate is estimated by using the deconvolution kernel density estimation. The parameters of the regression are estimated by the integrated squared distance based on the log ratio of the estimated density. We show the consistency and the asymptotic normality of the proposed estimators. Simulation study shows the superiority of the proposed method in different sample sizes and measurement error magnitudes scenario. The methodology is applied to estimating the relationship of systolic blood pressure and the presence of coronary heart disease.
Download or read book Logistic Regression written by David G. Kleinbaum and published by Springer Science & Business Media. This book was released on 2006-04-10 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the second edition of this text on logistic regression methods, ori- nally published in 1994. As in the first edition, each chapter contains a presentation of its topic in “lecture-book” format together with objectives, an outline, key formulae, practice exercises, and a test. The “lecture-book” has a sequence of illust- tions and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition has expanded the first edition by adding five new ch- ters and a new appendix. The five new chapters are Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data: GEE Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11–13 extend logistic regression to gen- alized estimating equations (GEE) and other methods for analyzing cor- lated response data. The appendix is titled “Computer Programs for Logistic Regression” and p- vides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The so- ware packages considered are SAS Version 8.0, SPSS Version 10.0, and STATA Version 7.0.
Download or read book Logistic Regression with Misclassified Response and Covariate Measurement Error written by Anna E. McGlothlin and published by . This book was released on 2007 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a variety of regression applications, measurement problems are unavoidable because infallible measurement tools may be expensive or unavailable. When modeling the relationship between a response variable and covariates, we must account for the uncertainty that is inherently introduced when one or both of these variables are measured with error. In this dissertation, we explore the consequences of and remedies for imperfect measurements. We consider a Bayesian analysis for modeling a binary outcome that is subject to misclassification. We investigate the use of informative conditional means priors for the regression coefficients. Additionally, we incorporate random effects into the model to accommodate correlated responses. Markov chain Monte Carlo methods are utilized to perform the necessary computations. We use the deviance information criterion to aid in model selection. Next, we consider data where measurements are flawed for both the response and explanatory variables. Our interest is in the case of a misclassified dichotomous response and a continuous covariate that is unobservable, but where measurements are available on its surrogate. A logistic regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. The methods developed are illustrated through an example. Results from a simulation experiment are provided illustrating advantages of the approach. Finally, we expand this model to incorporate random effects, resulting in a generalized linear mixed model for a misclassified response and covariate measurement error. We demonstrate the use of this model using a simulated data set.
Download or read book Safety Science Abstracts Journal written by and published by . This book was released on 1981 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Logistic Regression written by Scott W. Menard and published by SAGE. This book was released on 2010 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.
Download or read book American Doctoral Dissertations written by and published by . This book was released on 1979 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Logistic Regression with Covariate Measurement Error in an Adaptive Design written by JoAnna Christine Crixell and published by . This book was released on 2008 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive designs are increasingly popular in clinical trials. This is because such designs have the potential to decrease patient exposure to treatments that areless efficacious or unsafe. The Bayesian approach to adaptive designs is attractivebecause it makes systematic use of prior data and other information in a way that is consistent with the laws of probability. The goal of this dissertation is to examine the effects of measurement error on a Bayesian adaptive design. Measurement error problems are common in a variety of regression applications where the variable of interest cannot be measured perfectly. This is often unavoidable because infallible measurement tools to account for such error are either too expensive or unavailable. When modeling the relationship between a response variable and other covariates, we must account for any uncertainty introduced when one or both of these variables are measured with error. This dissertation will explore the consequence of imperfect measurements on a Bayesian adaptive design.
Download or read book Comprehensive Dissertation Index written by and published by . This book was released on 1984 with total page 856 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Logistic Regression written by David W. Hosmer, Jr. and published by Wiley-Interscience. This book was released on 1989-07-31 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shows how to model a binary outcome variable from a linear regression analysis point of view. Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation. Several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT.
Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 1995-07-06 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.
Download or read book Covariate Measurement Error in Logistic Regression written by L. A. Stefanski and published by . This book was released on 1985 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a logistic regression model when covariates are subject to measurement error the naive estimator, obtained by regressing on the observed covariates, is asymptotically biased. This document introduces a bias-adjusted estimator and two estimators appropriate for normally distributed measurement errors; a functional maximum likelihood estimator and an estimator which exploits the consequences of sufficiency. The four proposals are studied asymptotically under conditions which are appropriate when the measurement error is small. A small Monte-Carlo study illustrates the superiority of the measurement-error estimators in certain situations. Additional keywords: mathematical models.
Download or read book Measurement Error in Logistic Regression Model written by Sau Yee Lo and published by . This book was released on 2004 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Influence and Measurement Error in Logistic Regression written by Leonard A. Stefanski and published by . This book was released on 1983 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Comparison of Machine Learning Techniques in Predicting 10 year Risk of Coronary Heart Disease written by Adeola M. Olaniyan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The COVID-19 pandemic health crisis has necessitated a re-evaluation of medical health conditions. Otherwise "silent" conditions have been thrust into more awareness leading to an increase in research to identify mitigating measures. Previous studies have been carried out to develop models in predicting 10-year risk of CHD in patients using the Framingham data set. The current study is a comparison of models developed for the Framingham data set using six machine learning techniques to predict the 10-year risk of CHD. The model with the lowest test error and the highest prediction accuracy result was selected as the preferred model. The Framingham data set is obtained from an on-going longitudinal survey in Massachusetts. The supervised machine learning techniques utilized in this study include: multivariate logistic regression (MLR), linear discriminant analysis (LDA), classification tree, bagging, boosting and random forest algorithm. Both MLR and LDA are parametric models, while the other techniques are considered ensemble methods and non-parametric. The multivariate logistic regression model was selected as the preferred model due to its lowest test error of 0.149 and 85% prediction accuracy. The selected variables include: age, gender, systolic blood pressure, blood pressure medication, body mass index (BMI) and glucose concentration level in the body. Age, BMI, and systolic blood pressure were identified as the three most significant and recurring features in all the machine learning technique models. The analysis carried out does not reflect the age at which either a male or female patient's systolic reading can be interpreted to be in the high blood pressure range, leading to the risk of CHD (all other significant risk factors present). Rather, it identifies advancement in age as increasing the risk of CHD.
