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Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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Updated: May 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

DATA-ADAPTIVE EFFICIENT ESTIMATION STRATEGIES FOR BIOMARKER STUDIES EMBEDDED IN RANDOMIZED TRIALS.

Wei Zhang1, Zhiwei Zhang2, James F Troendle3

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences.

The Annals of Applied Statistics
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces efficient statistical methods for analyzing biomarker data from clinical trials. The new approaches improve accuracy in both complete and sampled biomarker data settings, aiding biomarker discovery and validation.

Keywords:
Augmentationprecision medicinesemiparametric theorysuper learnertwo-phase sampling

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Last Updated: May 21, 2026

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Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
07:40

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Biomarker Research

Background:

  • Predictive and prognostic biomarkers are crucial in clinical research.
  • Biomarker studies are often integrated into randomized clinical trials.
  • Biospecimens are collected at baseline and assayed for biomarkers.

Purpose of the Study:

  • To propose efficient estimation strategies for biomarker ascertainment in clinical trials.
  • To address both complete-data and two-phase sampling designs.
  • To develop robust statistical methods for biomarker analysis.

Main Methods:

  • Utilizing semiparametric theory to characterize efficient estimating functions.
  • Employing data-adaptive machine learning methods for approximation.
  • Developing estimators that are consistent, asymptotically normal, and efficient.

Main Results:

  • The proposed methods demonstrate efficiency in complete-data settings.
  • The strategies are effective for two-phase sampling designs with outcome-dependent subsampling.
  • Simulation studies and real-world data analyses validate the methods.

Conclusions:

  • The developed methods provide efficient and reliable tools for biomarker analysis in clinical trials.
  • These techniques enhance the statistical rigor of biomarker studies.
  • The findings support improved biomarker discovery and validation processes.