Assessing the coefficient of variations of chemical data using bootstrap method

Amiri, Saeid; Zwanzig, Silvelyn

doi:10.1002/cem.1350

Cited by 11 publications

(14 citation statements)

References 22 publications

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“…Parametric approaches have been used, but these methods make assumptions that may not be valid about the underlying distribution of the statistical parameter (Gupta and Ma 1996;Nairy and Rao 2003;Pang et al 2005). The bootstrapping (also known as resampling) technique (Amiri and Zwanzig 2011) used in this study overcomes these problems. However, this approach only provides information about whether results differ in comparison to those from the commercial analyzer but provides no information about the likely magnitude of differences; the results for one analyzer may vary more greatly than the commercial analyzer yet still be within the biological variation guidelines (as for ALT on the IDEXX and Heska analyzers).…”

Section: Discussionmentioning

confidence: 99%

“…Assessments compared to commercial laboratory analyzer CV A s for each analyte on each analyzer (for both QCMs) were compared to the CV A for that analyte on the commercial analyzer with a bootstrap technique implemented using R (http://www.r-project.org/) adapted from a previously published code (Amiri and Zwanzig 2011) to allow twosided interpretation.…”

Section: Assessments In Relation To Biological Variationmentioning

confidence: 99%

See 1 more Smart Citation

Repeatability of results from three in-house biochemistry analyzers and a commercial laboratory analyzer used in small animal practice

Baral

Morton²,

Dhand

et al. 2014

Comp Clin Pathol

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The repeatability (precision) of clinical pathology results is vital for confidence in the measured values. Comparison to biological variation is an accepted standard for instrument/method performance in human clinical pathology. This study aims to assess precision of biochemistry results from three in-house analyzers and one commercial laboratory analyzer in relation to biological variation and to compare precision between the in-house analyzers and the commercial laboratory analyzer. Two commercially available quality control materials (QCMs) were repeatedly tested on the same and different days. Coefficients of variation were calculated and assessed in relation to published biological variation data for cats and dogs. In-house analyzer results were compared to results from the commercial laboratory. Seven of 13 analytes assessed met desirable quality precision standards for one QCM and minimum quality standards for the other QCM on all analyzers for both cats and dogs. For more than half of all determinations across all in-house analyzers, precision was as good as or better than the commercial laboratory analyzer. The precision of results from the analyzers assessed for most of the analytes tested is generally high, so large differences between repeated results from the same patient are probably due to biological changes rather than analyzer variation.

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Section: Discussionmentioning

confidence: 99%

Section: Assessments In Relation To Biological Variationmentioning

confidence: 99%

Repeatability of results from three in-house biochemistry analyzers and a commercial laboratory analyzer used in small animal practice

Baral

Morton²,

Dhand

et al. 2014

Comp Clin Pathol

View full text Add to dashboard Cite

show abstract

“…F-tests were used to determine significance of fixed effects. Standard deviation in day and mean of peak shedding, as well as the number of days shedding, were analyzed using Levene’s test and a bootstrap method with a bootstrap value of 1000 (Amiri and Zwanzig, 2010,2011; Wargo et al, 2012). …”

Section: Methodsmentioning

confidence: 99%

Replication and shedding kinetics of infectious hematopoietic necrosis virus in juvenile rainbow trout

et al. 2017

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Viral replication and shedding are key components of transmission and fitness, the kinetics of which are heavily dependent on virus, host, and environmental factors. To date, no studies have quantified the shedding kinetics of infectious hematopoietic necrosis virus (IHNV) in rainbow trout (Oncorhynchus mykiss), or how they are associated with replication, making it difficult to ascertain the transmission dynamics of this pathogen of high agricultural and conservation importance. Here, the replication and shedding kinetics of two M genogroup IHNV genotypes were examined in their naturally co-evolved rainbow trout host. Within host virus replication began rapidly, approaching maximum values by day 3 post-infection, after which viral load was maintained or gradually dropped through day 7. Host innate immune response measured as stimulation of Mx-1 gene expression generally followed within host viral loads. Shedding also began very quickly and peaked within 2 days, defining a generally uniform early peak period of shedding from 1 to 4 days after exposure to virus. This was followed by a post-peak period where shedding declined, such that the majority offish were no longer shedding by day 12 post-infection. Despite similar kinetics, the average shedding rate over the course of infection was significantly lower in mixed compared to single genotype infections, suggesting a competition effect, however, this did not significantly impact the total amount of virus shed. The data also indicated that the duration of shedding, rather than peak amount of virus shed, was correlated with fish mortality. Generally, the majority of virus produced during infection appeared to be shed into the environment rather than maintained in the host, although there was more retention of within host virus during the post-peak period. Viral virulence was correlated with shedding, such that the more virulent of the two genotypes shed more total virus. This fundamental understanding of IHNV shedding kinetics and variation at the individual fish level could assist with management decisions about how to respond to disease outbreaks when they occur.

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“…The variability between groups was therefore compared using the coefficient of variation (CV) , defined here as the standard deviation/mean, making it a unitless parameter. Because the data was not normally distributed, the coefficient of variation of treatment groups was compared using a distribution-free bootstrap method proposed by Amiri and Zwanzig (Amiri and Zwanzig, 2010; Amiri and Zwanzig, 2011). Briefly, the p-value was calculated with the formula #:{T b (Y 1b * ,Y 2b * ) > T(Y 1 ,Y 2 )}/(B+1) .…”

Section: Methodsmentioning

confidence: 99%

“…In all cases, virus quantification occurred on day 3 post-infection when viral load typically peaks in the host (Peñaranda et al, 2009; Troyer et al, 2008). The differences in viral load variation between treatment groups were assessed by comparing coefficients of variation and conducting bootstrap tests (Amiri and Zwanzig, 2010; Amiri and Zwanzig, 2011). …”

Section: Introductionmentioning

confidence: 99%

Analysis of host genetic diversity and viral entry as sources of between-host variation in viral load

et al. 2012

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Little is known about the factors that drive the high levels of between-host variation in pathogen burden that are frequently observed in viral infections. Here, two factors thought to impact viral load variability, host genetic diversity and stochastic processes linked with viral entry into the host, were examined. This work was conducted with the aquatic vertebrate virus, Infectious hematopoietic necrosis virus (IHNV), in its natural host, rainbow trout. It was found that in controlled in vivo infections of IHNV, a suggestive trend of reduced between-fish viral load variation was observed in a clonal population of isogenic trout compared to a genetically diverse population of out-bred trout. However, this trend was not statistically significant for any of the four viral genotypes examined, and high levels of fish-to-fish variation persisted even in the isogenic trout population. A decrease in fish-to-fish viral load variation was also observed in virus injection challenges that bypassed the host entry step, compared to fish exposed to the virus through the natural water-borne immersion route of infection. This trend was significant for three of the four virus genotypes examined and suggests host entry may play a role in viral load variability. However, high levels of viral load variation also remained in the injection challenges. Together, these results indicate that although host genetic diversity and viral entry may play some role in between-fish viral load variation, they are not major factors. Other biological and non-biological parameters that may influence viral load variation are discussed.

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Assessing the coefficient of variations of chemical data using bootstrap method

Cited by 11 publications

References 22 publications

Repeatability of results from three in-house biochemistry analyzers and a commercial laboratory analyzer used in small animal practice

Repeatability of results from three in-house biochemistry analyzers and a commercial laboratory analyzer used in small animal practice

Replication and shedding kinetics of infectious hematopoietic necrosis virus in juvenile rainbow trout

Analysis of host genetic diversity and viral entry as sources of between-host variation in viral load

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