Literature DB >> 17382420

Analysing microbiological data: Tobit or not Tobit?

M F Lorimer1, A Kiermeier.   

Abstract

In this paper, methods used to collect microbiological data and the common statistical technique used to analyse such data are discussed. An alternative statistical approach, considered more appropriate for many microbial datasets is suggested. The effectiveness of this method is demonstrated using a simulation study and then, for a practical example, its application in a study on the effectiveness of a decontamination process for chicken carcases.

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Year:  2007        PMID: 17382420     DOI: 10.1016/j.ijfoodmicro.2007.02.001

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  11 in total

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Journal:  Lifetime Data Anal       Date:  2018-06-08       Impact factor: 1.588

2.  Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies.

Authors:  Getachew Dagne; Yangxin Huang
Journal:  Int J Biostat       Date:  2012-09-18       Impact factor: 0.968

3.  Thermal Stability of Hepatitis E Virus as Estimated by a Cell Culture Method.

Authors:  Reimar Johne; Eva Trojnar; Matthias Filter; Jörg Hofmann
Journal:  Appl Environ Microbiol       Date:  2016-06-30       Impact factor: 4.792

4.  Bayesian semiparametric mixture Tobit models with left censoring, skewness, and covariate measurement errors.

Authors:  Getachew A Dagne; Yangxin Huang
Journal:  Stat Med       Date:  2013-04-02       Impact factor: 2.373

5.  Extreme Heat Resistance of Food Borne Pathogens Campylobacter jejuni, Escherichia coli, and Salmonella typhimurium on Chicken Breast Fillet during Cooking.

Authors:  Aarieke E I de Jong; Esther D van Asselt; Marcel H Zwietering; Maarten J Nauta; Rob de Jonge
Journal:  Int J Microbiol       Date:  2012-01-29

6.  Non-Toxin-Producing Bacillus cereus Strains Belonging to the B. anthracis Clade Isolated from the International Space Station.

Authors:  Kasthuri Venkateswaran; Nitin K Singh; Aleksandra Checinska Sielaff; Robert K Pope; Nicholas H Bergman; Sandra P van Tongeren; Nisha B Patel; Paul A Lawson; Masataka Satomi; Charles H D Williamson; Jason W Sahl; Paul Keim; Duane Pierson; Jay Perry
Journal:  mSystems       Date:  2017-06-27       Impact factor: 6.496

7.  Microbial-Maximum Likelihood Estimation Tool for Microbial Quantification in Food From Left-Censored Data Using Maximum Likelihood Estimation for Microbial Risk Assessment.

Authors:  Gyung Jin Bahk; Hyo Jung Lee
Journal:  Front Microbiol       Date:  2021-12-24       Impact factor: 5.640

8.  Learning Something From Nothing: The Critical Importance of Rethinking Microbial Non-detects.

Authors:  Alex Ho Shing Chik; Philip J Schmidt; Monica B Emelko
Journal:  Front Microbiol       Date:  2018-10-05       Impact factor: 5.640

9.  Tracing back multidrug-resistant bacteria in fresh herb production: from chive to source through the irrigation water chain.

Authors:  Maria-Theresia Gekenidis; Ulrich Schöner; Ueli von Ah; Mathias Schmelcher; Fiona Walsh; David Drissner
Journal:  FEMS Microbiol Ecol       Date:  2018-11-01       Impact factor: 4.194

10.  Lead content in wild game shot with lead or non-lead ammunition - Does "state of the art consumer health protection" require non-lead ammunition?

Authors:  Antje Gerofke; Ellen Ulbig; Annett Martin; Christine Müller-Graf; Thomas Selhorst; Carl Gremse; Markus Spolders; Helmut Schafft; Gerhard Heinemeyer; Matthias Greiner; Monika Lahrssen-Wiederholt; Andreas Hensel
Journal:  PLoS One       Date:  2018-07-26       Impact factor: 3.240

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