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Data integration: exploiting ratios of parameter estimates from a reduced external model.

Jeremy M G Taylor1, Kyuseong Choi2, Peisong Han1

  • 1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48019, U.S.A.

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Summary
This summary is machine-generated.

This study introduces a new statistical method for parameter estimation in generalized linear models using both internal and external data. The approach enhances efficiency and robustness by borrowing information from external studies under specific transportability conditions.

Keywords:
Data integrationOmitted variable regressionRatio of parametersTransportability

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Generalized linear models (GLMs) are widely used for analyzing binary outcomes.
  • Estimating model parameters often relies solely on internal data, potentially limiting efficiency.
  • External data can offer valuable insights but integrating it poses methodological challenges.

Purpose of the Study:

  • To develop a novel method for parameter estimation in GLMs using both internal and external datasets.
  • To propose a technique that makes minimal assumptions about population distribution similarity.
  • To enhance the efficiency and robustness of parameter estimation compared to using internal data alone.

Main Methods:

  • The proposed method involves orthogonalizing covariates and borrowing information on the ratio of coefficients from an external GLM.
  • It leverages a new theoretical result connecting parameters in GLMs with and without omitted covariates.
  • Applicability relies on checking the similarity of regression coefficients across populations, up to a scalar constant.

Main Results:

  • The method demonstrates increased efficiency compared to analyzing only the internal dataset in simulation studies.
  • It shows robustness when compared to alternative methods for incorporating external information.
  • Asymptotic variance of the proposed method was derived and evaluated.

Conclusions:

  • The developed method offers a statistically sound approach to integrate external data for GLM parameter estimation.
  • It provides efficiency gains and robustness, particularly when regression coefficients exhibit scalar transportability.
  • This approach advances the use of external information in statistical modeling for binary outcomes.