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Model-assisted estimators for time-to-event data from complex surveys.

Benjamin M Reist1, Richard Valliant2

  • 1Office of the CIO, National Aeronautics and Space Administration, Washington, DC, USA.

Statistics in Medicine
|September 30, 2020
PubMed
Summary
This summary is machine-generated.

New survey estimators improve accuracy for event occurrence by time t. These model-assisted methods leverage predictive covariates, reducing standard errors for better population estimates in health studies.

Keywords:
doubly robustgeneral difference estimatormodel calibrated estimatortime-to-failure model

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Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Estimating population event proportions over time is crucial for public health.
  • Traditional survey methods can have limitations in precision, especially with complex data.
  • Integrating predictive covariates can enhance estimation accuracy.

Purpose of the Study:

  • To develop and evaluate novel model-assisted estimators for time-to-event data in complex surveys.
  • To compare the performance of new estimators against traditional methods.
  • To demonstrate the utility of these estimators in a real-world health study.

Main Methods:

  • Development of model-assisted estimators based on time-to-event models.
  • Simulation studies comparing new estimators with conventional survey estimation techniques.
  • Application of estimators to the Nurses' Health Study data.

Main Results:

  • The proposed model-assisted estimators demonstrated improved precision.
  • Reduced standard errors were observed compared to traditional alternatives.
  • The estimators effectively utilized predictive covariates to enhance accuracy.

Conclusions:

  • Model-assisted estimators offer a robust approach for analyzing complex survey data with time-to-event outcomes.
  • These methods provide a valuable tool for improving the accuracy of population health estimates.
  • The approach is effective in leveraging auxiliary information for more precise results.