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Biostatistics and Bayes
Vol. 5, No. 3 (Aug., 1990), pp. 269-284
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2245811
Page Count: 16
You can always find the topics here!Topics: Statism, Statistical estimation, Biometrics, Clinical trials, Statistics, Modeling, Disease risks, Tumors, Inference, Epidemiology
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Attitudes of biostatisticians toward implementation of the Bayesian paradigm have changed during the past decade due to the increased availability of computational tools for realistic problems. Empirical Bayes' methods, already widely used in the analysis of longitudinal data, promise to improve cancer incidence maps by accounting for overdispersion and spatial correlation. Hierarchical Bayes' methods offer a natural framework in which to demonstrate the bioequivalence of pharmacologic compounds. Their use for quantitative risk assessment and carcinogenesis bioassay is more controversial, however, due to uncertainty regarding specification of informative priors. Bayesian methods simplify the analysis of data from sequential clinical trials and avoid certain paradoxes of frequentist inference. They offer a natural setting for the synthesis of expert opinion in deciding policy matters. Both frequentist and Bayes' methods have a place in biostatistical practice.
Statistical Science © 1990 Institute of Mathematical Statistics