Causal inference with measurement error in outcomes: Bias analysis and estimation methods.

Area of Science:

• Biostatistics
• Epidemiology
• Statistical Methods

Background:

• Inverse probability weighting (IPW) is a common method for estimating average treatment effects (ATE).
• The validity of IPW is compromised by measurement error in outcome variables.
• Existing methods often fail to account for mismeasured outcomes, leading to biased estimates.

Purpose of the Study:

• To investigate the impact of measurement error on IPW estimation for both continuous and binary outcome variables.
• To develop novel, consistent estimation procedures for ATE in the presence of mismeasured outcomes.
• To propose efficient and robust methods utilizing validation data or replicates, and addressing model misspecification.

Main Methods:

• Analysis of measurement error models for continuous (additive error) and binary outcomes.
• Development of closed-form bias derivation for binary outcomes.
• Proposal of efficient estimation using validation data.
• Introduction of a doubly robust estimator to handle potential model misspecification.

Main Results:

• Naive analysis may yield consistent estimators for continuous outcomes under additive error but introduces bias for binary outcomes.
• Proposed methods provide consistent and efficient ATE estimation with validation data.
• The doubly robust estimator demonstrates robustness against treatment or outcome model misspecification.
• Simulation studies confirm the performance of the developed methods.

Conclusions:

• Measurement error in outcome variables significantly impacts IPW estimation, necessitating specialized methods.
• The developed techniques offer reliable and efficient estimation of average treatment effects even with imperfect outcome data.
• The proposed doubly robust approach enhances the applicability of IPW in complex epidemiological and clinical research settings.