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Updated: Oct 9, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Two-phase stratified sampling and analysis for predicting binary outcomes.

Yaqi Cao1, Sebastien Haneuse2, Yingye Zheng3

  • 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA and Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China.

Biostatistics (Oxford, England)
|December 19, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new resource allocation strategy for two-phase study designs, improving risk prediction tool evaluation. The method enhances efficiency for estimating predictive accuracy measures, crucial for clinical tools.

Keywords:
Post-stratificationReceiver operating characteristic (ROC) curveRisk predictionSemiparametric maximum likelihoodTwo-phase design

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Two-phase study designs are cost-efficient for expensive data elements.
  • Existing resource allocation guidance prioritizes association parameter estimation.
  • This focus may be suboptimal for developing risk prediction tools.

Purpose of the Study:

  • To propose a novel resource allocation strategy for two-phase studies.
  • To enhance efficiency in estimating predictive accuracy measures for risk prediction tools.
  • To develop methods for valid estimation and inference using biased samples.

Main Methods:

  • Oversampling cases with extreme risk estimates from a preliminary model.
  • Extending semiparametric maximum likelihood methods for biased sampling.
  • Proposing a general post-stratification scheme for analyzing two-phase data.
  • Validating methods through theoretical calculations and simulation studies.

Main Results:

  • The proposed sampling strategy improves efficiency for predictive accuracy estimation.
  • The post-stratification scheme enables valid analysis of two-phase data.
  • Demonstrated efficiency gains through theoretical and simulation studies.
  • Successfully applied methods to a real-world cardiac surgery readmission risk model.

Conclusions:

  • The novel strategy effectively enhances efficiency for risk prediction tool evaluation.
  • The proposed methods provide valid estimation and inference for predictive accuracy.
  • This approach offers a more appropriate resource allocation for prediction tool development in two-phase studies.