Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification
| Autor: | Christine Müller-Graf, Matthias Greiner, Matthias Flor, Michael Weiß, Thomas Selhorst |
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| Rok vydání: | 2020 |
| Předmět: |
Misclassification
Bayesian probability Prevalence estimation 01 natural sciences Diagnostic specificity 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Frequentist inference Statistics Prevalence Credible interval Statistical inference Humans Medicine 030212 general & internal medicine Point estimation Truncation (statistics) 0101 mathematics Bayes estimator business.industry lcsh:Public aspects of medicine Public Health Environmental and Occupational Health Bayes Theorem lcsh:RA1-1270 Confidence interval Diagnostic sensitivity Imperfect diagnostic test Bayesian prevalence estimate Rogan-Gladen estimate business Research Article |
| Zdroj: | BMC Public Health, Vol 20, Iss 1, Pp 1-10 (2020) BMC Public Health |
| DOI: | 10.21203/rs.2.23469/v3 |
| Popis: | Background Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals’ coverage. Methods In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method. Results Across all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advantages of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE’s truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval. |
| Databáze: | OpenAIRE |
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