Bayesian perspectives for epidemiological research. II. Regression analysis
نویسندگان
چکیده
منابع مشابه
Bayesian perspectives for epidemiological research. II. Regression analysis.
This article describes extensions of the basic Bayesian methods using data priors to regression modelling, including hierarchical (multilevel) models. These methods provide an alternative to the parsimony-oriented approach of frequentist regression analysis. In particular, they replace arbitrary variable-selection criteria by prior distributions, and by doing so facilitate realistic use of impr...
متن کاملCommentary: on Bayesian perspectives for epidemiological research.
semi-Bayes analyses of conditional-logistic and proportional-hazards regression. The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. Being skeptical about meta-analyses: a Bayesian perspective on magnesium trials in myocardial infarction. exposures: a review and a comparative stud...
متن کاملBayesian perspectives for epidemiological research: I. Foundations and basic methods.
One misconception (of many) about Bayesian analyses is that prior distributions introduce assumptions that are more questionable than assumptions made by frequentist methods; yet the assumptions in priors can be more reasonable than the assumptions implicit in standard frequentist models. Another misconception is that Bayesian methods are computationally difficult and require special software. ...
متن کاملBayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods.
I present some extensions of Bayesian methods to situations in which biases are of concern. First, a basic misclassification problem is illustrated using data from a study of sudden infant death syndrome. Bayesian analyses are then given. These analyses can be conducted directly, or by converting actual-data records to incomplete records and prior distributions to complete-data records, then ap...
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Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data. Methods: This study use...
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ژورنال
عنوان ژورنال: International Journal of Epidemiology
سال: 2007
ISSN: 1464-3685,0300-5771
DOI: 10.1093/ije/dyl289