2003
DOI: 10.1016/s1471-4922(03)00149-1
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Mixed models: getting the best use of parasitological data

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Cited by 127 publications
(108 citation statements)
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“…The influence of sample characteristics on identification results was assessed in a mixed-model approach accounting for cluster effects due to the inclusion of multiple aerobic and anaerobic culture bottles per patient (leading to pseudoreplicate observations on isogenic bacteria) (33). Using the MIXED procedure from the SPSS statistics software (version 16; SPSS Inc., Chicago), identification scores from direct mass spectrometry fingerprinting were modeled as a function of sample characteristics (culture condition, culture age, date of measurement, Gram reactivity, and type of organism) as fixed effects and replication as a random effect.…”
Section: Methodsmentioning
confidence: 99%
“…The influence of sample characteristics on identification results was assessed in a mixed-model approach accounting for cluster effects due to the inclusion of multiple aerobic and anaerobic culture bottles per patient (leading to pseudoreplicate observations on isogenic bacteria) (33). Using the MIXED procedure from the SPSS statistics software (version 16; SPSS Inc., Chicago), identification scores from direct mass spectrometry fingerprinting were modeled as a function of sample characteristics (culture condition, culture age, date of measurement, Gram reactivity, and type of organism) as fixed effects and replication as a random effect.…”
Section: Methodsmentioning
confidence: 99%
“…GLMM was used because it allowed us to pool all native bird species, by using individual infection status (infected or not) as the dependent variable, while controlling for sample size difference. Thus, information is not lost due to sample size restriction, since more weight will be given to data with larger sample size (Paterson andLello 2003, Jovani andTella 2006). In addition, GLMM is a powerful method to analyse parasitological data because it allows for the use of data that are not normally distributed, such as presence and absence data (infected or not), while controlling for correlations between measures that occur as a result of grouped observations (Paterson and Lello 2003).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Thus, information is not lost due to sample size restriction, since more weight will be given to data with larger sample size (Paterson andLello 2003, Jovani andTella 2006). In addition, GLMM is a powerful method to analyse parasitological data because it allows for the use of data that are not normally distributed, such as presence and absence data (infected or not), while controlling for correlations between measures that occur as a result of grouped observations (Paterson and Lello 2003). Furthermore, according to Sodhi et al (2008), GLMM are more appropriate than independent contrast analysis when categorical variables are included in the analysis, hence species can be used as a random effect to encompass variation among species and therefore control for phylogenetic effects (Bolker et al 2009).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Effects of treatment on the amount of DM, ADF, N, and TP were analysed using a mixed model analysis of variance with site as a random factor and treatment and month as fixed factors (Bennington and Thayne 1994;Paterson and Lello 2003). Interactions were allowed up to the second degree, as higher degree interactions are difficult to interpret.…”
Section: Data Treatment and Statistical Analysesmentioning
confidence: 99%