A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data.
Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing...
| Publicado en: | Annals of Occupational Hygiene Vol. 60; no. 1; pp. 56 - 74 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111952942&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111952942 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00034878 JQ4 jtl: Annals of Occupational Hygiene issn: 00034878 maglogo: N pubinfo: dt: Jan2016 vid: 60 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 111952942 111952942 111952942 10.1093/annhyg/mev049 111952942 ppf: 56 ppct: 18 formats: fmt: @attributes: type: P tig: atl: A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data. aug: au: Tran Huynh Quick, Harrison Ramachandran, Gurumurthy Banerjee, Sudipto Stenzel, Mark Sandler, Dale P. Engel, Lawrence S. Kwok, Richard K. Blair, Aaron Stewart, Patricia A. affil: Division of Environmental Health Sciences, University of Minnesota, Minneapolis, MN 55455, USA sug: subj: Occupational Exposure Evaluation Models, Statistical Methods Probability Survival Analysis Sensitivity and Specificity Parametric Statistics Descriptive Statistics ab: Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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