An Accurate Substitution Method for Analyzing Censored Data.
When analyzing censored datasets, where one or more measurements are below the limit of detection (LOD), the maximum likelihood estimation (MLE) method is often considered the gold standard for estimating the GM and GSD of the underlying exposure profile. A new and relatively simple substitution met...
| Publicado en: | Journal of Occupational & Environmental Hygiene Vol. 7; no. 4; pp. 233 - 245 |
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| Autores principales: | , |
| Formato: | CEU equations & formulas research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2010
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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=103905818&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103905818 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15459624 V1L jtl: Journal of Occupational & Environmental Hygiene issn: 15459624 maglogo: Y pubinfo: dt: Apr2010 vid: 7 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 103905818 97634999 10.1080/15459621003609713 103905818 ppf: 233 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An Accurate Substitution Method for Analyzing Censored Data. aug: au: Ganser, Gary H. Hewett, Paul affil: Department of Mathematics, West Virginia University, Morgantown, West Virginia. sug: subj: Data Analysis, Statistical Methods Occupational Exposure Human Algorithms Computer Simulation Education, Continuing (Credit) ab: When analyzing censored datasets, where one or more measurements are below the limit of detection (LOD), the maximum likelihood estimation (MLE) method is often considered the gold standard for estimating the GM and GSD of the underlying exposure profile. A new and relatively simple substitution method, called β-substitution, is presented and compared with the MLE method and the common substitution methods (LOD/2 and LOD/ √ 2 substitution) when analyzing a left-censored dataset with either single or multiple censoring points. A computer program was used to generate censored exposure datasets for various combinations of true geometric standard deviation (1.2 to 4), percent censoring (1% to 50%), and sample size (5 to 19 and 20 to 100). Each method was used to estimate four parameters of the lognormal distribution: (1) the geometric mean, GM; (2) geometric standard deviation, GSD; (3) 95th percentile, and (4) Mean for the censored datasets. When estimating the GM and GSD, the bias and root mean square error (rMSE) for the β-substitution method closely matched those for the MLE method, differing by only a small amount, which decreased with increasing sample size. When estimating the Mean and 95th percentile the β-substitution method bias results closely matched or bettered those for the MLE method. In addition, the overall imprecision, as indicated by the rMSE, was similar to that of the MLE method when estimating the GM, GSD, 95th percentile, and Mean. The bias for the common substitution methods was highly variable, depending strongly on the range of GSD values. The β- substitution method produced results comparable to the MLE method and is considerably easier to calculate, making it an attractive alternative. In terms of bias it is clearly superior to the commonly used LOD/2 and LOD/ √ 2 substitution methods. The rMSE results for the two substitution methods were often comparable to rMSE results for the MLE method, but the substitution methods were often considerably biased. pubtype: Academic Journal doctype: CEU equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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