The Experts below are selected from a list of 82308 Experts worldwide ranked by ideXlab platform
Cristy L Bird - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage "prior" Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage “prior” Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates. (Epidemiology 1994;5:612–621)
John S Witte - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage "prior" Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage “prior” Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates. (Epidemiology 1994;5:612–621)
Rex P. Bringula - One of the best experts on this subject based on the ideXlab platform.
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Influence of faculty- and web portal design-related factors on web portal usability: A Hierarchical Regression analysis
Computers & Education, 2013Co-Authors: Rex P. BringulaAbstract:This study determined the influence of faculty- and web portal design-related factors on web portal usability. Descriptive statistics revealed that most of the respondents were in their early 40's, had Master's degree, had Internet access at home, were committed to the use of the web portal, had been using the web portal for more than 4 semesters, and were intermediate users. They perceived that it was evident that the web portal was designed in terms of ease of use, information content, availability, speed, and aesthetics. Both e-learning services and library online resources were only used from time to time. The fourth step of Hierarchical Regression analysis showed age could only influence web portal usability provided the users were committed to the use of the web portal. The last step revealed that age, commitment to the use of the web portal, and information content found to influence web portal usability. Thus, the fourth and fifth null hypotheses were partially rejected. It was concluded that commitment was a strong positive ''force'' that could push older people to use Internet technologies, and technical and non-technical aspects influence web portal usability. Implications were also presented.
Constantine Gatsonis - One of the best experts on this subject based on the ideXlab platform.
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a Hierarchical Regression approach to meta analysis of diagnostic test accuracy evaluations
Statistics in Medicine, 2001Co-Authors: Carolyn M Rutter, Constantine GatsonisAbstract:An important quality of meta-analytic models for research synthesis is their ability to account for both within- and between-study variability. Currently available meta-analytic approaches for studies of diagnostic test accuracy work primarily within a fixed-effects framework. In this paper we describe a Hierarchical Regression model for meta-analysis of studies reporting estimates of test sensitivity and specificity. The model allows more between- and within-study variability than fixed-effect approaches, by allowing both test stringency and test accuracy to vary across studies. It is also possible to examine the effects of study specific covariates. Estimates are computed using Markov Chain Monte Carlo simulation with publicly available software (BUGS). This estimation method allows flexibility in the choice of summary statistics. We demonstrate the advantages of this modelling approach using a recently published meta-analysis comparing three tests used to detect nodal metastasis of cervical cancer.
Robert W Haile - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage "prior" Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates.
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Hierarchical Regression analysis applied to a study of multiple dietary exposures and breast cancer
Epidemiology, 1994Co-Authors: John S Witte, Sander Greenland, Robert W Haile, Cristy L BirdAbstract:Hierarchical Regression attempts to improve standard Regression estimates by adding a second-stage “prior” Regression to an ordinary model. Here, we use Hierarchical Regression to analyze case-control data on diet and breast cancer. This Regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic Regression, our Hierarchical Regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates. (Epidemiology 1994;5:612–621)
