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

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Titel:	Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses.
Autoren:	Edwards JK; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Shook-Sa BE; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Bakoyannis G; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Zivich PN; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Herce ME; School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Cole SR; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Quelle:	Statistics in medicine [Stat Med] 2025 Oct; Vol. 44 (23-24), pp. e70281.
Publikationsart:	Journal Article
Sprache:	English
Info zur Zeitschrift:	Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):	Original Publication: Chichester ; New York : Wiley, c1982-
MeSH-Schlagworte:	Causality*, Humans ; Incidence ; Bias ; Computer Simulation ; Cause of Death ; Acquired Immunodeficiency Syndrome/mortality ; Acquired Immunodeficiency Syndrome/drug therapy ; Models, Statistical ; Cohort Studies ; Anti-HIV Agents/therapeutic use ; Confidence Intervals
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. (© 2025 John Wiley & Sons Ltd.)
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Grant Information:	R01AI157758 United States NH NIH HHS; K01AI182506 United States NH NIH HHS; K01AI177102 United States NH NIH HHS
Contributed Indexing:	Keywords: HIV; causality; mortality; outcome measurement errors
Substance Nomenclature:	0 (Anti-HIV Agents)
Entry Date(s):	Date Created: 20251007 Date Completed: 20251007 Latest Revision: 20251007
Update Code:	20251007
DOI:	10.1002/sim.70281
PMID:	41055568
Datenbank:	MEDLINE

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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.<br /> (© 2025 John Wiley & Sons Ltd.)
ISSN:	1097-0258
DOI:	10.1002/sim.70281