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Model misspecification and robust analysis for outcome-dependent sampling designs under generalized linear models.

Jacob M Maronge1, Jonathan S Schildcrout2, Paul J Rathouz3

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Statistics in Medicine
|February 9, 2023
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Summary
This summary is machine-generated.

Outcome-dependent sampling (ODS) improves efficiency when exposure data is costly. This study introduces semi-parametric generalized linear models for robust analysis in two-phase studies, enhancing estimation accuracy.

Keywords:
efficiencygeneralized linear modelsoutcome-dependent samplingsemi-parametric modelstwo-phase studies

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Outcome-dependent sampling (ODS) is crucial for efficient estimation when exposure data is expensive.
  • Two-phase studies collect response and covariate data in Phase One, then selectively collect exposure data in Phase Two.
  • Phase Two samples are not representative, requiring specialized analysis to correct for ascertainment bias.

Purpose of the Study:

  • To develop robust likelihood-based analysis procedures for two-phase studies using ODS.
  • To extend existing methods by incorporating semi-parametric generalized linear models (SPGLM).
  • To improve robustness against misspecified distributional assumptions, particularly in generalized linear models (GLM).

Main Methods:

  • Focus on likelihood-based procedures: conditional-likelihood and full-likelihood approaches.
  • Implementation of a novel semi-parametric extension to generalized linear models (SPGLM).
  • Application to two-phase study designs with outcome-dependent sampling.

Main Results:

  • The proposed SPGLM approach offers improved robustness to distributional misspecification.
  • Likelihood-based procedures are adapted for non-representative Phase Two samples.
  • The methods provide flexible tools for settings with non-standard GLM assumptions.

Conclusions:

  • Semi-parametric methods enhance the reliability of ODS in two-phase studies.
  • This work provides practical guidance for handling complex sampling designs and distributional assumptions.
  • The developed tools are valuable for researchers facing challenges with expensive exposure data and non-standard statistical models.