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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

131
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Related Experiment Video

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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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A flexible quasi-likelihood model for microbiome abundance count data.

Yiming Shi1, Huilin Li2, Chan Wang2

  • 1Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA.

Statistics in Medicine
|August 22, 2023
PubMed
Summary

We introduce a flexible quasi-likelihood model for analyzing microbiome count data, offering valid statistical inference without strict distribution assumptions. This method, validated in simulations and a real adenoma study, is available via the R package "fql".

Keywords:
heteroscedasticityskewnesssplinezero-inflation

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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Area of Science:

  • Microbiome Research
  • Statistical Modeling
  • Bioinformatics

Background:

  • Microbiome count data often lack clear distributional properties, posing challenges for standard statistical models.
  • Existing methods like Poisson and negative binomial generalized linear models (GLMs) rely on specific distribution assumptions.
  • Accurate analysis of microbiome data is crucial for understanding host-microbe interactions and disease pathogenesis.

Purpose of the Study:

  • To present a flexible quasi-likelihood modeling framework for microbiome count data.
  • To evaluate the inferential validity of the proposed method against established GLMs.
  • To demonstrate the practical application of the method in a human microbiome study.

Main Methods:

  • Development of a quasi-likelihood framework that assumes only a smooth mean-variance relationship.
  • Comparison of the proposed model with negative binomial GLM and Poisson GLM via simulation studies.
  • Application of the method to analyze the relationship between adenomas and microbiota in a real-world dataset.

Main Results:

  • The flexible quasi-likelihood method demonstrated valid inferential results in simulation studies.
  • The model successfully identified relationships between adenomas and microbiota in the applied study.
  • The proposed method offers a robust alternative when distributional assumptions of traditional GLMs are uncertain.

Conclusions:

  • The flexible quasi-likelihood model provides a robust and valid approach for microbiome count data analysis.
  • The developed R package 'fql' facilitates the application of this novel method.
  • This approach enhances the ability to study complex host-microbiome interactions.