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Addressing confounding and continuous exposure measurement error using corrected score functions.

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Summary
This summary is machine-generated.

This study introduces new statistical methods to simultaneously address confounding and exposure measurement error in research. These techniques improve the accuracy of estimating exposure effects, particularly for complex health outcomes.

Keywords:
HIV/AIDScausal inferenceconfoundingcorrected score functionsmeasurement error

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Confounding and exposure measurement error are significant sources of bias in observational studies.
  • Existing methods often address these biases separately, but they frequently co-occur, necessitating simultaneous approaches.

Purpose of the Study:

  • To develop and evaluate statistical methods for simultaneously correcting confounding and exposure measurement error.
  • To enable accurate inference on marginal exposure effects using only measured variables.

Main Methods:

  • Derivation of corrected score methods under classical additive measurement error.
  • Proposal of three estimators: g-formula, inverse probability weighting, and doubly-robust estimation.
  • Implementation in the R package 'mismex'.

Main Results:

  • The proposed estimators are consistent and asymptotically normal.
  • The doubly-robust estimator demonstrates its namesake property.
  • Simulation studies confirm good performance in finite samples under confounding and measurement error.

Conclusions:

  • The developed methods effectively address simultaneous confounding and measurement error.
  • The doubly-robust estimator provides a robust approach for marginal effect estimation.
  • Application to HIV-1 biomarker data from the HVTN 505 trial demonstrates practical utility.