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Diagnostic methods for uncovering outcome dependent visit processes.

Charles E McCulloch1, John M Neuhaus1

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA USA.

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Summary
This summary is machine-generated.

Researchers developed diagnostic tools to identify outcome-dependent data in electronic health records. Ignoring this data bias can skew clinical research findings, impacting treatment effectiveness studies.

Keywords:
BiasInformative visit processMaximum likelihoodMissing not at random

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Clinical Research Methodology

Background:

  • Electronic health records (EHRs) are increasingly used for clinical research, offering real-world data.
  • Data availability in EHRs can be influenced by patient-reported outcomes, creating potential bias.
  • Ignoring outcome-dependent visit processes can lead to significant bias in longitudinal data models.

Purpose of the Study:

  • To develop and evaluate methods for detecting outcome-dependent visit processes in EHR data.
  • To provide tools for researchers to identify and address potential biases in clinical research using EHRs.

Main Methods:

  • Development of a score statistic to guide diagnostic test design.
  • Proposal and evaluation of various diagnostic approaches for outcome dependence.
  • Assessment of diagnostic test performance in simulated and real-world data scenarios.

Main Results:

  • Simple diagnostic tests demonstrate high power in identifying outcome-dependent visit processes.
  • Outcome dependence introduces bias in regression coefficients, particularly affecting generalized estimating equations (GEE) methods.
  • The developed diagnostic tests can detect bias before it substantially impacts likelihood-based methods.

Conclusions:

  • Effective tools are crucial for recognizing outcome dependence in EHR data for reliable clinical research.
  • Early detection of outcome dependence is vital to prevent biased regression coefficient estimates.
  • The proposed diagnostic methods offer a practical solution for improving the validity of EHR-based research.