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Related Concept Videos

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Related Experiment Video

Updated: Jan 8, 2026

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice
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TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.

Tianchen Xu1, Kun Chen2, Gen Li3

  • 1Bristol Myers Squibb.

The Annals of Applied Statistics
|October 3, 2024
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Summary
This summary is machine-generated.

This study introduces a new method to analyze complex longitudinal microbiome data, even with missing values. The approach reveals connections between infant gut microbiome development and neurodevelopmental outcomes.

Keywords:
Scalar-on-tensor regressionblockwise missing datalongitudinal measurementsmicrobiome data

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Area of Science:

  • Biostatistics
  • Microbiome Research
  • Longitudinal Data Analysis

Background:

  • Multivariate longitudinal data with blockwise missing values present significant challenges for association analysis.
  • Existing methods often fail with incomplete datasets, necessitating new analytical approaches.

Purpose of the Study:

  • To develop a novel statistical method for associating high-dimensional, longitudinal microbiome data with continuous outcomes, addressing blockwise missingness.
  • To leverage tensor-based regression for analyzing complex, incomplete longitudinal data.

Main Methods:

  • Representing multivariate longitudinal data as a three-way tensor (sample-by-feature-by-time).
  • Employing a parsimonious scalar-on-tensor regression model.
  • Developing a regularized covariance-based estimation procedure to handle missing data without imputation.

Main Results:

  • The proposed method effectively handles blockwise missing values in longitudinal data.
  • Variable selection and smooth estimation of time-varying effects were achieved.
  • Significant associations were found between preterm infant gut microbiome dynamics and neurodevelopment.

Conclusions:

  • The scalar-on-tensor regression model provides an effective framework for analyzing incomplete multivariate longitudinal data.
  • The method offers insights into the relationship between microbiome dynamics and neurodevelopment in infants.
  • The approach is validated through applications in microbiome, synthetic, and aging studies.