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Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
Open accessAbstract
Summary: The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate function of the counting process without the Poisson-type assumption. In this paper, we provide a rigorous justification of such robust procedures through modern empirical process theory. Furthermore, we present an approach to constructing simultaneous confidence bands for the mean function and describe a class of graphical and numerical techniques for checking the adequacy of the fitted mean–rate model. The advantages of the robust procedures are demonstrated through simulation studies. An illustration with multiple-infection data taken from a clinical study on chronic granulomatous disease is also provided.
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“An additional pre-specified analysis, the Andersen-Gill model using a Cox proportional-hazard with the counting-process formulation, was performed to model the total events (50,51).”
From Effects of Icosapent Ethyl on Total Ischemic Events (2019) · cites this paper as [51]
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Metadata
- Authors
- Lin, D. Y., Wei, L. J., Yang, I., & Ying, Z.
- Affiliations
- University of Washington (US)
- Harvard University (US)
- Schering-Plough Research Institute , Kenilworth , USA
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Lin, D. Y.
- ORCID
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-11-01
- Volume
- 62
- Issue
- 4
- Pages
- 711-730
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2012053212
- Cited by
- 969
- References
- 18
- Citations by year
- 2026: 47 · 2025: 67 · 2024: 70 · 2023: 57 · 2022: 56 · 2021: 51 · 2020: 55 · 2019: 44 · 2018: 53 · 2017: 38 · 2016: 36 · 2015: 45 · 2014: 55 · 2013: 38 · 2012: 38
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UNC Libraries
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- recurrent event data, semiparametric regression, mean function, rate function, counting processes, empirical process theory, simultaneous confidence bands, model adequacy assessment, co-infection, chronic granulomatous disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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