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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Statistical inference for high-dimensional generalized estimating equations.

Lu Xia1, Ali Shojaie2

  • 1Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, East Lansing, MI, 48824, United States.

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|May 5, 2026
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Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing complex, high-dimensional correlated data, particularly useful in omics research. The procedure provides reliable confidence intervals for associations, improving insights from large datasets like those in COVID-19 proteomics.

Keywords:
Confidence intervalcorrelated datade-biased estimatorhypothesis testingprojected estimating equation

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Area of Science:

  • Biostatistics
  • Genomics
  • Proteomics

Background:

  • Analyzing correlated data with many variables is challenging, especially with limited sample sizes.
  • High-throughput omics data, like proteomics, often present this high-dimensional correlated structure.
  • Existing methods struggle with inference for high-dimensional regression coefficients in generalized estimating equations.

Purpose of the Study:

  • To develop a novel inference procedure for linear functionals of high-dimensional regression coefficients.
  • To address the analysis of correlated omics data, motivated by COVID-19 studies.
  • To introduce a data-driven method for selecting tuning parameters in high-dimensional settings.

Main Methods:

  • Developed a novel inference procedure using projected estimating equations.
  • Established asymptotic normality of the proposed estimator under mild conditions.
  • Introduced a cross-validation technique for tuning parameter selection.

Main Results:

  • The proposed estimator is asymptotically normally distributed.
  • Demonstrated robust finite-sample performance in simulations, particularly in bias and coverage.
  • Successfully applied the procedure to provide confidence intervals for protein-COVID risk associations.

Conclusions:

  • The novel procedure offers a statistically sound approach for high-dimensional correlated data analysis.
  • The method enhances confidence interval estimation for associations in omics studies.
  • The data-driven cross-validation improves practical application and reliability.