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Wald-Wolfowitz Runs Test II01:17

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Investigation of the Transcriptional Role of a RUNX1 Intronic Silencer by CRISPR/Cas9 Ribonucleoprotein in Acute Myeloid Leukemia Cells
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Deletion diagnostics for alternating logistic regressions.

John S Preisser1, Kunthel By, Jamie Perin

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, CB 7420, Chapel Hill, NC 27599-7420, USA. jpreisse@bios.unc.edu

Biometrical Journal. Biometrische Zeitschrift
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

New deletion diagnostics help analyze clustered binary data using alternating logistic regressions. These methods accurately assess the impact of data clusters on regression models.

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Clustered binary outcomes are common in health research.
  • Alternating logistic regressions (ALR) are used for analyzing such data.
  • Existing diagnostics for generalized estimating equations (GEE) may not fully apply to ALR.

Purpose of the Study:

  • To introduce novel deletion diagnostics for ALR.
  • To assess the influence of individual clusters on model parameters and fit.
  • To provide computationally efficient diagnostic formulas.

Main Methods:

  • Developed diagnostics within an estimating equations framework.
  • Recasted estimating functions using conditional residuals to marginal residuals.
  • Derived one-step deletion diagnostic formulas.
  • Evaluated diagnostics through simulation studies and a real-world data application.

Main Results:

  • The proposed cluster-deletion diagnostics approximate exact iterative diagnostics well.
  • Simulations confirmed the utility and accuracy of the new diagnostics.
  • The diagnostics effectively measured the influence of clusters on regression parameters and model fit.

Conclusions:

  • The new cluster-deletion diagnostics are valuable tools for analyzing clustered binary data with ALR.
  • These diagnostics offer reliable and efficient methods for model diagnostics.
  • The findings support the use of these diagnostics in health services research and other fields.