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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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An augmented probit model for missing predictable covariates in quantal bioassay with small sample size.

Dean Follmann1, Martha Nason

  • 1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, 6700B Rockledge Drive MSC 7609, Bethesda, Maryland 20892, USA. dfollmann@niaid.nih.gov

Biometrics
|January 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to accurately measure drug potency using both direct and proxy assays. The method improves HIV vaccine research by providing a reliable estimate of the infectious dose (ID(1)).

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Area of Science:

  • Biostatistics
  • Immunology
  • Pharmacology

Background:

  • Quantal bioassays link compound potency to binary outcomes like infection or death in animal models.
  • Probit regression is standard for infectious disease assays, with ID(P) indicating the dose for P% infection.
  • Errors-in-variables problems arise when using proxy measures alongside direct dose measurements in validation sets.

Purpose of the Study:

  • To develop a statistical model addressing errors-in-variables in quantal bioassays with validation sets.
  • To estimate the distribution of direct measures conditional on proxy measures.
  • To derive a pseudo-likelihood for probit regression and apply parametric bootstrap for inference.

Main Methods:

  • Developed a constrained seemingly unrelated regression (SUR) model for validation sets.
  • Derived a pseudo-likelihood function based on the conditional distribution.
  • Employed parametric bootstrap for statistical inference and compared with regression calibration and joint likelihood.

Main Results:

  • Successfully estimated the ID(1) for a new neutralizing antibody (nAB) assay against HIV.
  • The method provides a more accurate measure of potency compared to traditional approaches when using proxy data.
  • Simulations demonstrated the pseudo-likelihood estimates' performance against alternative methods.

Conclusions:

  • The proposed statistical approach effectively handles errors-in-variables in bioassay data.
  • This method enhances the estimation of critical potency measures like ID(1), crucial for vaccine development.
  • The study successfully re-evaluated an HIV antibody experiment, yielding a key estimate for vaccine candidates.