Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses

Misclassification between causes of death can produce bias in estimated cumulative incidence functions. When estimating causal quantities, such as comparing the cumulative incidence of death due to specific causes under interventions, such bias can lead to suboptimal decision making. Here, a consistent semiparametric estimator of the cumulative incidence function under interventions in settings with misclassification between two event types is presented. The measurement parameters for this estimator can be informed by validation data or expert knowledge. Moreover, a modified bootstrap approach to variance estimation is proposed for confidence interval construction. The proposed estimator was applied to estimate the cumulative incidence of AIDS-related mortality in the Multicenter AIDS Cohort Study under single- versus combination-drug antiretroviral therapy regimens that may be subject to confounding. The proposed estimator is shown to be consistent and performed well in finite samples via a series of simulation experiments.

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Publication Details

Journal
Statistics in Medicine
Published
2025-10-01
DOI
https://doi.org/10.1002/sim.70281
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses

Bonnie E. Shook‐Sa, Jessie K. Edwards, Giorgos Bakoyannis, Michael E. Herce et al.
Statistics in Medicine
Advanced Causal Inference Techniques
article

Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses

Bonnie E. Shook‐Sa, Jessie K. Edwards, Giorgos Bakoyannis, Michael E. Herce, Paul N. Zivich, Stephen R. Cole
article en

Abstract

Misclassification between causes of death can produce bias in estimated cumulative incidence functions. When estimating causal quantities, such as comparing the cumulative incidence of death due to specific causes under interventions, such bias can lead to suboptimal decision making. Here, a consistent semiparametric estimator of the cumulative incidence function under interventions in settings with misclassification between two event types is presented. The measurement parameters for this estimator can be informed by validation data or expert knowledge. Moreover, a modified bootstrap approach to variance estimation is proposed for confidence interval construction. The proposed estimator was applied to estimate the cumulative incidence of AIDS-related mortality in the Multicenter AIDS Cohort Study under single- versus combination-drug antiretroviral therapy regimens that may be subject to confounding. The proposed estimator is shown to be consistent and performed well in finite samples via a series of simulation experiments.

Statistics in MedicineVol. 44(23-24)
University of North Carolina at Chapel Hill (US), Indiana University Indianapolis (US), Indiana University – Purdue University Indianapolis (US)
National Institutes of Health, National Institute of Allergy and Infectious Diseases
Openalex Percentile: Top 74%
Advanced Causal Inference Techniques
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