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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Regression analysis for multiple-disease group testing data.

Boan Zhang1, Christopher R Bilder, Joshua M Tebbs

  • 1Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, U.S.A.

Statistics in Medicine
|May 25, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces novel regression methods for group testing, enabling accurate individual disease status determination from pooled samples. The new techniques address correlated disease statuses in multi-disease screening, improving cost-effective public health surveillance.

Keywords:
Infertility Prevention Projectcorrelated binary dataexpectation-solution algorithmgeneralized estimating equationspooled testingspecimen pooling

Related Experiment Videos

Last Updated: May 11, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Group testing is a cost-effective strategy for screening large populations by pooling individual specimens.
  • Analyzing group testing data presents challenges due to the need for individual-level inferences from group-level results.
  • Simultaneous testing for multiple diseases introduces complexity due to potential correlations in individual disease statuses.

Purpose of the Study:

  • To develop advanced regression techniques for analyzing multiple-disease group testing data.
  • To provide a method for accurate individual disease status inference when only group test results are available.
  • To address the challenge of correlated individual disease statuses in simultaneous group testing scenarios.

Main Methods:

  • Proposed novel regression techniques specifically designed for multiple-disease group testing.
  • Developed an expectation-solution based algorithm for parameter estimation and inference.
  • The algorithm ensures consistent parameter estimates and facilitates natural large-sample inference procedures.

Main Results:

  • The proposed methodology yields consistent parameter estimates for individual disease statuses.
  • The developed algorithm supports robust large-sample inference procedures.
  • Successful application demonstrated on real-world datasets for chlamydia, gonorrhea, and prenatal infectious diseases.

Conclusions:

  • The new regression techniques effectively handle the complexities of multiple-disease group testing.
  • The methodology offers a significant advancement for cost-effective disease screening and surveillance.
  • This approach enhances the ability to infer individual health statuses from pooled testing data.