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An application of bayesian analysis to medical follow‐up data
Author(s) -
Achcar Jorge A.,
Brookmeyer Ron,
Hunter William G.
Publication year - 1985
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780040411
Subject(s) - weibull distribution , bayesian probability , prior probability , computer science , posterior probability , scale (ratio) , data set , statistics , data mining , mathematics , artificial intelligence , cartography , geography
Posterior distributions can provide effective summaries of the main conclusions of medical follow‐up studies. In this article, we use Bayesian methods for the analysis of survival data. We describe posterior distributions for various parameters of clinical interest in the presence of arbitrary right censorship. Non‐informative reference priors result from transformation of a two‐parameter Weibull model into a location‐scale family. We suggest an approach for checking adequacy. For illustration, we apply the methods to a well‐known acute leukemia data set.