Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]<nowiki>; </nowiki>[#_ENREF_4 Enøe et al., 2000]<nowiki>; </nowiki>[#_ENREF_10 Johnson et al., 2001]<nowiki>; </nowiki>[#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
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Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#11 Joseph et al., 1995]; [#4 Enøe et al., 2000]; [#10 Johnson et al., 2001]; [#2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#7 Georgiadis et al., 2003]).
Revisi terkini pada 10 Mei 2015 14.06
Bayesian estimation
Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#11 Joseph et al., 1995]; [#4 Enøe et al., 2000]; [#10 Johnson et al., 2001]; [#2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#7 Georgiadis et al., 2003]).
