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Multistate analysis from cross-sectional and auxiliary samples.

Leilei Zeng1, Richard J Cook1, Jooyoung Lee1

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.

Statistics in Medicine
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

Cross-sectional sampling in epidemiological studies can bias results. This research analyzes naive logistic regression models and suggests using auxiliary samples for accurate multistate disease process analysis.

Keywords:
Markov modelauxiliary datacross-sectional sampleintensity functionmultistage disease process

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Disease Modeling

Background:

  • Epidemiological studies often use cross-sectional sampling.
  • Individuals in these studies undergo life history processes.
  • Stationarity assumptions are common in analyzing disease progression.

Purpose of the Study:

  • To analyze features of cross-sectional samples in multistate disease processes.
  • To investigate the behavior of estimators in naive logistic regression models.
  • To explore the utility of auxiliary samples for comprehensive life history modeling.

Main Methods:

  • Utilizing intensity functions for progressive multistate disease models.
  • Studying limiting values of regression coefficient estimators.
  • Conducting simulations to validate asymptotic results.
  • Evaluating the necessity and application of auxiliary data.

Main Results:

  • Naive logistic regression models may yield biased estimates in cross-sectional samples.
  • Asymptotic results are confirmed through simulations, offering insights for finite samples.
  • Auxiliary samples are crucial for accurately modeling the full multistate life history process.

Conclusions:

  • Cross-sectional sampling in epidemiological studies requires careful consideration due to potential biases.
  • Auxiliary data enhances the ability to model complex life history processes.
  • Findings are applicable to real-world data, such as assessing psoriatic arthritis markers.