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Sensitivity analysis for studies transporting prediction models.

Jon A Steingrimsson1, Sarah E Robertson2,3, Sarah Voter1

  • 1Department of Biostatistics, Brown University, Providence, RI 02903, United States.

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Summary
This summary is machine-generated.

This study introduces a sensitivity analysis to assess model performance in a target population when outcome data is missing. It addresses uncertainty in the conditional independence assumption, crucial for reliable predictions.

Keywords:
exponential tiltmodel performanceprediction modelssensitivity analysistransportability

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Estimating model performance in a target population is challenging when only covariate data is available.
  • Existing methods rely on an untestable conditional independence assumption between outcomes and population.
  • This assumption's uncertainty necessitates robust sensitivity analysis.

Purpose of the Study:

  • To develop a sensitivity analysis framework for evaluating model performance under assumption violations.
  • To propose an exponential tilt sensitivity model for quantifying the impact of assumption violations.
  • To provide statistical methods for estimating model performance in the target population.

Main Methods:

  • Developed an exponential tilt sensitivity analysis model.
  • Derived identification results and estimators for risk in the target population.
  • Examined the large-sample properties of the proposed estimators.
  • Applied the methods to lung cancer screening data.

Main Results:

  • The proposed methods quantify the impact of conditional independence assumption violations on model performance measures.
  • Identification and estimation of target population risk are possible under the sensitivity model.
  • The approach was successfully applied to real-world lung cancer screening data.

Conclusions:

  • Sensitivity analysis is crucial for assessing the reliability of model performance estimates when outcome data is missing.
  • The exponential tilt model provides a flexible framework for evaluating assumption violations.
  • This work offers valuable tools for biostatisticians and epidemiologists working with external validation data.