Abstract
For subject i, we monitor an event that can occur multiple times over a random observation window [0, (Formula presented.)). At each recurrence, p concomitant variables, (Formula presented.), associated to the event recurrence are recorded—a subset ((Formula presented.)) of which is measured with errors. To circumvent the problem of bias and consistency associated with parameter estimation in the presence of measurement errors, we propose inference for corrected estimating equations with well-behaved roots under an additive measurement errors model. We show that estimation is essentially unbiased under the corrected profile likelihood for recurrent events, in comparison to biased estimations under a likelihood function that ignores correction. We propose methods for obtaining estimators of error variance and discuss the properties of the estimators. We further investigate the case of mis specified error models and show that the resulting estimators under misspecification converge to a value different from that of the true parameter—thereby providing a basis for bias assessment. We demonstrate the foregoing correction methods on an open source rhDNase dataset gathered in a clinical setting.
Recommended Citation
R. Alahakoon et al., "Estimation And Model Misspecification For Recurrent Event Data With Covariates Under Measurement Errors," Mathematics, vol. 13, no. 1, article no. 113, MDPI, Jan 2025.
The definitive version is available at https://doi.org/10.3390/math13010113
Department(s)
Mathematics and Statistics
Publication Status
Open Access
Keywords and Phrases
corrected score; covariate measurement errors; Kullback–Leibler divergence; model misspecification; recurrent events
International Standard Serial Number (ISSN)
2227-7390
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2025
