Inverse sampling regression for pooled data.
Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing pre...
| Published in: | Statistical Methods in Medical Research Vol. 26; no. 3; pp. 1093 - 1110 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Sage Publications Inc.
Jun2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123549777&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123549777 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jun2017 vid: 26 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 123549777 123549777 NLM25601742 10.1177/0962280214568047 NLM25601742 123549777 ppf: 1093 ppct: 17 formats: tig: atl: Inverse sampling regression for pooled data. aug: au: Montesinos-López, Osval A. Montesinos-López, Abelardo Eskridge, Kent Crossa, Joséa affil: Facultad de Telemática, Universidad de Colima, Colima, México sug: ab: Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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