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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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The Binomial Theorem is a foundational principle in algebra used to expand expressions raised to a power. It provides a structured approach for expanding binomials of the form (a+b)n, where a and b are variables or constants representing algebraic expressions, and n is a non-negative integer.The general form of the Binomial Theorem is:Each term in the expansion involves a binomial coefficient, which is calculated using factorials:The exponent of a in each term decreases from n to 0, while the...
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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Expanding a binomial expression such as (a + b)n results in a predictable sequence of terms that can be systematically derived using Pascal’s Triangle. This triangular array of numbers plays a central role in understanding and computing the coefficients of binomial expansions.Pascal’s Triangle is constructed such that each row corresponds to the coefficients of a binomial raised to a power. The topmost row, known as the zeroth row, corresponds to (a + b)0, and each successive row...
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Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data.

Xinyan Zhang1, Yu-Fang Pei2, Lei Zhang2

  • 1Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, United States.

Frontiers in Microbiology
|August 11, 2018
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Summary
This summary is machine-generated.

This study introduces Negative Binomial Mixed Models (NBMMs) to analyze longitudinal microbiome data, effectively handling complex biological variations and time-dependent correlations for more accurate insights into microbial dynamics.

Keywords:
count datalongitudinal studymetagenomicsmicrobiomenegative binomial mixed model

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Microbiome sequencing data offers insights into host-microbe interactions and microbial dynamics over time.
  • Microbiome data analysis is complicated by varied sequence reads, over-dispersion, zero-inflation, and longitudinal structures.

Purpose of the Study:

  • To propose Negative Binomial Mixed Models (NBMMs) for analyzing longitudinal microbiome data.
  • To develop an efficient algorithm for fitting NBMMs and demonstrate their utility.

Main Methods:

  • Application of Negative Binomial Mixed Models (NBMMs) to account for over-dispersion, varying total reads, and longitudinal correlations.
  • Development of an efficient and stable algorithm for fitting NBMMs.
  • Evaluation through extensive simulation studies and a real-world longitudinal microbiome dataset.

Main Results:

  • NBMMs effectively handle over-dispersion and varying total reads in microbiome data.
  • The proposed method accurately models dynamic trends and correlations in longitudinal samples.
  • NBMMs outperform existing methods in flexibility for correlation structures and detecting dynamic effects.

Conclusions:

  • NBMMs provide a robust framework for analyzing longitudinal microbiome data.
  • The developed R package NBZIMM offers a valuable tool for researchers studying microbial dynamics.
  • This method enhances the understanding of microbiome associations with host and environmental factors over time.