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Semiparametric Regression for the Mean and Rate Functions of Recurrent Events

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Lin, D. Y., Wei, L. J., Yang, I., Ying, Z.·2000·Journal of the Royal Statistical Society Series B: Statistical Methodology

DOI 10.1111/1467-9868.00259

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Abstract

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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Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
The Statistical Analysis of Failure Time Data.
Aitkin, M. A., Kalbfleisch, J. D., & Prentice, R. L. (1981) · Biometrics · doi:10.2307/2530581 · Cited by 9,875
Approximation Theorems of Mathematical Statistics
Serfling, R. J. (1980) · Wiley series in probability and statistics · doi:10.1002/9780470316481 · Cited by 5,908
Weak Convergence and Empirical Processes
van der Vaart, A. W., & Wellner, J. A. (1996) · Springer Series in Statistics · doi:10.1007/978-1-4757-2545-2 · Cited by 5,014
Cox’s regression model for counting processes: A large sample study
Andersen, P. K., & Gill, R. D. (1982) · The Annals of Statistics · doi:10.1214/aos/1176345976 · Cited by 4,419
Empirical Processes with Applications to Statistics
Shorack, G. R., & Wellner, J. A. (2009) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719017 · Cited by 2,310
Counting Processes and Survival Analysis.
Dabrowska, D. M., Fleming, T. R., & Harrington, D. P. (1992) · Journal of the American Statistical Association · doi:10.2307/2290673 · Cited by 2,287
Regression analysis of multivariate incomplete failure time data by modeling marginal distributions
Wei, L. J., Lin, D. Y., & Weissfeld, L. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478873 · Cited by 1,868
Self-citation
Checking the Cox model with cumulative sums of martingale-based residuals
LIN, D. Y., WEI, L. J., & YING, Z. (1993) · Biometrika · doi:10.1093/biomet/80.3.557 · Cited by 1,432
Self-citation
Probability Theory: Independence, Interchangeability, Martingales
Lund, R., Chow, Y. S., & Teicher, H. (1998) · Journal of the American Statistical Association · doi:10.2307/2670078 · Cited by 1,095

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Achcar, J. A., & Barili, E. (2023) · Statistical Methods & Applications · doi:10.1007/s10260-023-00734-w · Cited by 1
Effectiveness of Bivalent Boosters Over Nine and Half Months
Paritala, S., Xu, Y., Du, Y., Donahue, M. A., Maloney, P., & Lin, D.-Y. (2023) · Journal of Biotechnology and Biomedicine · doi:10.26502/jbb.2642-91280121 · Cited by 1
Self-citation
Aspects of the analysis of multivariate failure time data
Prentice, R. L., & Kalbfleisch, J. D. (2003) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1
Statistical Methods on Survival Data with Measurement Error
Yan, Y. (2014) · UWSpace (University of Waterloo) · Cited by 1
Statistical Methods for Life History Analysis Involving Latent Processes
Shen, H. (2014) · UWSpace (University of Waterloo) · Cited by 1
Statistical Models and Methods for Dependent Life History Processes
Lee, J. (2018) · UWSpace (University of Waterloo) · Cited by 1
Robust Methods for Estimating the Intraclass Correlation Coefficient and for Analyzing Recurrent Event Data
Chen, T. (2018) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1
Semi-parametric and Parametric Methods for the Analysis of Multi-center Survival Data.
He, Z. (2012) · Deep Blue (University of Michigan) · Cited by 1
Restricted Mean Analysis Across Multiple Follow-up Intervals.
Tayob, N. (2013) · Deep Blue (University of Michigan)
Non- and semi-parametric Bayesian inference with recurrent events and coherent systems data
Rahman, A. K. M. F. (2014) · Scholar Commons (University of South Carolina)
Multistate Intensity Model with AR-GARCH Random Effect for Corporate Credit Rating Transition Analysis
Li, Z. (2010)
Regression Modeling of Longitudinal Outcomes With Outcome-Dependent Observation Times
Tan, K. S. (2014) · ScholarlyCommons (University of Pennsylvania)
Remarks on Some Estimation Methods
Källén, A. (2011) · Wiley eBooks · doi:10.1002/9781119992677.ch13
New Semiparametric Methods For Recurrent Events Data
Gu, Y. (2011)
Estimating the Effects of Air Pollutants on Recurrent Hospital Admission for Respiratory Diseases
Shan, Q. (2013) · University Library - University of Saskatchewan (University of Saskatchewan)
Regression Analysis of Panel Count Data I
Sun, J., & Zhao, X. (2013) · Statistics for biology and health · doi:10.1007/978-1-4614-8715-9_5
Model checking techniques for regression models in cancer screening
Ghosh, D. (2004) · Collection of Biostatistics Research Archive
Analysis of Marked Recurrent Events in the Presence of a Terminating Event.
Ma, Y. (2012) · Deep Blue (University of Michigan)
On sample selection models and skew distributions
Ogundimu, E. O. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33632461
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Cheng, G. (2012) · doi:10.17077/etd.8tj1kn7f
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Gong, Q., & Schaubel, D. E. (2014) · Statistics in Biosciences · doi:10.1007/s12561-014-9115-3
Estimation Methods of a Joint Model Based on Proportional Intensity Function and Proportional Hazard Function
Liu, H. B., Yin, T., & Rao, C. J. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.631-632.27
Sufficient dimension reduction on marginal regression for gaps of recurrent events
Zhao, X., & Zhou, X. (2014) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2014.01.008
Comparisons between simultaneous and componentwise splines for varying coefficient models
Chiang, C. (2005) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf02915430
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Liu, H. B., Yin, T., & Wang, C. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.631-632.3
Variance Estimation for Statistics Computed from Single Recurrent Event Processes
Guan, Y., Yan, J., & Sinha, R. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01559.x
Nonparametric Inference for the Recurrent Event Data with Incomplete Observation Gaps
Kim, J., Nam, C. M., & Kim, Y. (2012) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2012.25.4.621
Weighted Estimator for the Linear Transformation Models with Multivariate Failure Time Data
Qiu, Z., Makhija, N., & Zhou, Y. (2014) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2013.844254
Robust Correlation Structure for Multivariate Failure Time Data
Hasan, M. T., & Sneddon, G. (2008) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610910802245726
Nonparametric inference for the joint distribution of recurrent marked variables and recurrent survival time
Yee, L. M., & Chan, K. C. G. (2015) · Lifetime Data Analysis · doi:10.1007/s10985-015-9347-7
Recurrent Event Data
Stocker, R. S. (2008) · Encyclopedia of Quantitative Risk Analysis and Assessment · doi:10.1002/9780470061596.risk0473
Some Nonparametric Methods for Clinical Trials and High Dimensional Data
Wu, X. (2011) · doi:10.7916/d87087fz
Hazards Regression for Cancer Studies
Shen, P. (2008) · Communication in Statistics- Theory and Methods · doi:10.1080/03610920802127063
Nonparametric comparison of recurrent event processes based on panel count data
Xu, D., Sun, J., & Wang, D. (2015) · Journal of the Korean Statistical Society · doi:10.1016/j.jkss.2015.11.001
method and the modifled within-cluster resampling method
Luo, X., & Huang, C. (2009)
Copula-graphic estimators for the marginal survival function with censoring indicators missing at random
Liu, Y., & Wang, Q. (2015) · Statistics & Probability Letters · doi:10.1016/j.spl.2015.08.010
Joint modeling of longitudinal data with a dependent terminal event
Sui, H., Du, T., & Sun, L. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2013.851237
More efficient estimators for marginal additive hazards model in case-cohort studies with multiple outcomes
Wang, J., & Zhou, J. (2016) · Acta Mathematica Sinica English Series · doi:10.1007/s10114-016-5162-7
Attributable Risk Function in the Proportional Hazards Model
Chen, Y. Q., Hu, C., & Wang, Y. Q. (2005) · Collection of Biostatistics Research Archive
RECURRENT EVENT MODELS IN THE PRESENCE OF A TERMINAL EVENT: COMPARISON, INFERENCE AND DATA ANALYSIS
Luo, X., & Wang, M. (2006) · Collection of Biostatistics Research Archive
Linear transformation models for survival analysis with tumor growth information in cancer screening study
Shen, P. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2015.1030425
The proportional hazards model for multiple type recurrent gap times
Liu, J., Liu, H., & Zhang, R. (2016) · Acta Mathematicae Applicatae Sinica English Series · doi:10.1007/s10255-016-0551-3
Some issues in marginal recurrent event Cox type models
McLain, A. C., & Peña, E. A. (2008) · NCSU Libraries Repository (North Carolina State University Libraries)
A New Modelling for Recurrent Event Data Under Informative Censoring
Ünlü, H. K., & Yîḡîter, A. (2016) · Turkiye Klinikleri Journal of Biostatistics · doi:10.5336/biostatic.2016-50770
Semiparametric and nonparametric methods for the analysis of panel count data
Li, Y. (2013) · doi:10.32469/10355/43244
Non parametric analysis of gap times for bivariate recurrent event data with cure fraction
Shen, P. (2017) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2017.1301473
Median Analysis of Repeated Measures Associated with Recurrent Events in Presence of Terminal Event
Sundaram, R., Ma, L., & Ghoshal, S. (2017) · The International Journal of Biostatistics · doi:10.1515/ijb-2016-0057
Marginal Methods for Multivariate Time to Event Data
Wu, L. (2012) · UWSpace (University of Waterloo)
An additive marginal regression model for clustered recurrent event in the presence of a terminal event
Kang, F. (2017) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2017.1332221
MEDICAID ENROLLMENT PATTERNS AND SERVICE UTILIZATION OF EPILEPSY PATIENTS IN OHIO
Bakaki, P. M. (2013) · OhioLink ETD Center (Ohio Library and Information Network)
Nonparametric Marginal Analysis of Recurrent Events Data under Competing RisksPreprint
Li, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01822
Hazard and Rate Regression in the Presence of Differential Selection or Termination Probability.
Pan, Q. (2007) · Deep Blue (University of Michigan)
Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study
Tan, P. L., Ibrahim, N. A., Adam, M. B., & Arasan, J. (2018) · Journal of Fundamental and Applied Sciences · doi:10.4314/jfas.v9i6s.13
Semiparametric Functional Temporal Process Regression of Prevalence Outcomes
Zhan, T. (2017) · Deep Blue (University of Michigan)
Methods for Direct Modeling of Restricted Mean Survival Time for General Censoring Mechanisms and Causal Inference
Wang, X. (2017) · Deep Blue (University of Michigan)
Commentary: Alignment of time scales and joint models
Chan, K. C. G. (2018) · Lifetime Data Analysis · doi:10.1007/s10985-018-9446-3
Partial sufficient dimension reduction on joint model of recurrent and terminal events
Wang, W.-W., Wu, X., Zhang, X., Zhao, X., & Zhou, X. (2018) · Journal of Applied Statistics · doi:10.1080/02664763.2018.1506019
Statistical inference for the accelerated failure time model under two-stage generalized case–cohort design
Cao, Y., Shi, Y., & Yu, J. (2018) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2018.1528363
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Mütze, T. (2018) · doi:10.53846/goediss-7186
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Jiang, Q., Zhao, H., & Qin, H. (2019) · Acta Mathematicae Applicatae Sinica English Series · doi:10.1007/s10255-019-0831-9
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Bhattacharya, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09746
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References
(2013) · Wiley series in probability and statistics · doi:10.1002/9781118458167.refs
References
Lachin, J. M. (2010) · Wiley series in probability and statistics · doi:10.1002/9780470907412.refs
References
(2012) · Wiley eBooks · doi:10.1002/9781118307656.refs
Bibliography
(2002) · Wiley series in probability and statistics · doi:10.1002/9781118032985.biblio
Repeated Events
Lawless, J. F. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a11065
References
(2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801342-7.00019-8
Nonparametric Estimation
Sun, J., & Zhao, X. (2013) · Statistics for biology and health · doi:10.1007/978-1-4614-8715-9_3
Index of Authors
(2000) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00270
Regression Analysis of Panel Count Data II
Sun, J., & Zhao, X. (2013) · Statistics for biology and health · doi:10.1007/978-1-4614-8715-9_6
Repeated Events
Lawless, J. F. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat06054
Introduction
Sun, J., & Zhao, X. (2013) · Statistics for biology and health · doi:10.1007/978-1-4614-8715-9_1
A Simultaneous Estimation of the Baseline Intensity and Parameters for Modulated Renewal Processes
Zhuang, J., & Siew, H.-Y. (2022) · Axioms · doi:10.3390/axioms11070303
Estimating restricted mean treatment effects with additive-multiplicative hazards models
Li, J., Liu, J., Pei, Y., & Zhang, R. (2022) · Journal of nonparametric statistics · doi:10.1080/10485252.2022.2108810
Analyzing recurrent and nonrecurrent terminal events data in discrete time
Wen, C., & Chen, Y. (2022) · Biometrical Journal · doi:10.1002/bimj.202100361
Estimation of an Additive Rate Model for Clustered Recurrent Events Based on the Piecewise Constant Method
郭, 蒋. (2023) · Advances in Applied Mathematics · doi:10.12677/aam.2023.122057
Nonparametric estimation of marked survival data in the presence of dependent censoring
Sanusi, B. O., Cai, J., & Hudgens, M. G. (2023) · Statistics in Medicine · doi:10.1002/sim.9710

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: Lin, D. Y.
Wei, L. J.
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
DOI: 10.1111/1467-9868.00259
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

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Semiparametric Regression for the Mean and Rate Functions of Recurrent Events

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