Semiparametric transformation models for multivariate panel count data with dependent observation process
This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recu...
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| Published in | Canadian journal of statistics Vol. 39; no. 3; pp. 458 - 474 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.09.2011
Statistical Society of Canada Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0319-5724 1708-945X 1708-945X |
| DOI | 10.1002/cjs.10118 |
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| Abstract | This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. Cet article discute de l'analyse de régression pour les données panels multidimensionnelles de dénombrement pour lesquelles le processus observationnel peut contenir de l'information sur le processus des événements récurrents sous-jacents. De telles données se produisent lorsque l'étude des événements récurrents implique plusieurs types d'événements et que le processus ou schéma observationnel peut dépendre des sujets. Pour ce problème, une classe de modèles semi-paramétriques de transformation est présentée ce qui permet une grande flexibilité pour modéliser l'effet des covariables sur le processus des événements récurrents. Pour l'estimation des paramètres de régression, nous développons une procédure d'inférence basée sur des équations d'estimation et nous obtenons aussi les propriétés asymptotiques des estimateurs résultants. Finalement, l'approche proposée est évaluée à l'aide d'études de simulation et nous l'appliquons à des données provenant d'un essai sur la chimioprévention du cancer de la peau. |
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| AbstractList | This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject‐specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation‐based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. The Canadian Journal of Statistics 39: 458–474; 2011 © 2011 Statistical Society of Canada Cet article discute de l'analyse de régression pour les données panels multidimensionnelles de dénombrement pour lesquelles le processus observationnel peut contenir de l'information sur le processus des événements récurrents sous‐jacents. De telles données se produisent lorsque l'étude des événements récurrents implique plusieurs types d'événements et que le processus ou schéma observationnel peut dépendre des sujets. Pour ce problème, une classe de modèles semi‐paramétriques de transformation est présentée ce qui permet une grande flexibilité pour modéliser l'effet des covariables sur le processus des événements récurrents. Pour l'estimation des paramètres de régression, nous développons une procédure d'inférence basée sur des équations d'estimation et nous obtenons aussi les propriétés asymptotiques des estimateurs résultants. Finalement, l'approche proposée est évaluée à l'aide d'études de simulation et nous l'appliquons à des données provenant d'un essai sur la chimioprévention du cancer de la peau. La revue canadienne de statistique 39:458–474;2011 © 2011 Société statistique du Canada This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. Cet article discute de l'analyse de régression pour les données panels multidimensionnelles de dénombrement pour lesquelles le processus observationnel peut contenir de l'information sur le processus des événements récurrents sous-jacents. De telles données se produisent lorsque l'étude des événements récurrents implique plusieurs types d'événements et que le processus ou schéma observationnel peut dépendre des sujets. Pour ce problème, une classe de modèles semi-paramétriques de transformation est présentée ce qui permet une grande flexibilité pour modéliser l'effet des covariables sur le processus des événements récurrents. Pour l'estimation des paramètres de régression, nous développons une procédure d'inférence basée sur des équations d'estimation et nous obtenons aussi les propriétés asymptotiques des estimateurs résultants. Finalement, l'approche proposée est évaluée à l'aide d'études de simulation et nous l'appliquons à des données provenant d'un essai sur la chimioprévention du cancer de la peau. This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial.This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. [PUBLICATION ABSTRACT] This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial. The Canadian Journal of Statistics 39: 458-474; 2011 ? 2011 Statistical Society of Canada Cet article discute de l'analyse de regression pour les donnees panels multidimensionnelles de denombrement pour lesquelles le processus observationnel peut contenir de l'information sur le processus des evenements recurrents sous-jacents. De telles donnees se produisent lorsque l'etude des evenements recurrents implique plusieurs types d'evenements et que le processus ou schema observationnel peut dependre des sujets. Pour ce probleme, une classe de modeles semi-parametriques de transformation est presentee ce qui permet une grande flexibilite pour modeliser l'effet des covariables sur le processus des evenements recurrents. Pour l'estimation des parametres de regression, nous developpons une procedure d'inference basee sur des equations d'estimation et nous obtenons aussi les proprietes asymptotiques des estimateurs resultants. Finalement, l'approche proposee est evaluee a l'aide d'etudes de simulation et nous l'appliquons a des donnees provenant d'un essai sur la chimioprevention du cancer de la peau. La revue canadienne de statistique 39:458-474; 2011 ? 2011 Societe statistique du Canada |
| Author | Li, Ni Kim, KyungMann Park, Do-Hwan Sun, Jianguo |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22685368$$D View this record in MEDLINE/PubMed |
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| References | Thall, P. F. & Lachin, J. M. (1988). Analysis of recurrent events: Nonparametric methods for random-interval count data. Journal of the American Statistical Association, 83, 339-347. Chen, B. E., Cook, R. J., Lawless, J. F., & Zhan, M. (2005). Statistical methods for multivariate interval-censored recurrent events. Statistics in Medicine, 24, 671C691. Sun, J. & Kalbfleisch, J. D. (1993). The analysis of current status data on point processes. Journal of the American Statistical Association, 88, 1449-1454. He, X., Tong, X., & Sun, J. (2009). Semiparametric analysis of panel count data with correlated observation and follow-up times. Lifetime Data Analysis, 15, 177-196. Lin, D. Y., Wei, L. J., Yang, I., & Ying, Z. (2000). Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society, Series B, 62, 711-730. Zhang, Y. (2002). A semiparametric pseudolikelihood estimation method for panel count data. Biometrika, 89, 39-48. Andersen, P. K., Borgan, O., Gill, R. D., & Keiding, N. (1993). Statistical Models Based on Counting Processes, Springer-Verlag, New York. He, X., Tong, X., Sun, J., & Cook, R. J. (2008). Regression analysis of multivariate panel count data. Biostatistics, 9, 234-248. Sun, J., Park, D., Sun, L. & Zhao, X. (2005). Semiparametric regression analysis of longitudinal data with informative observation times. Journal of the American Statistical Association, 100, 882-889. Lin, D. Y., Wei, L. J., & Ying, Z. (2001). Semiparametric transformation models for point processes. Journal of the American Statistical Association, 96, 620-628. Sun, J. & Kalbfleisch, J. D. (1995). Estimation of the mean function of point processes based on panel count data. Statistica Sinica, 5, 279-290. Cook, R. J. & Lawless, J. F. (2007). The Statistical Analysis of Recurrent Events, Springer-Verlag: New York. Huang, C.Y., Wang, M.C., & Zhang, Y. (2006). Analysing panel count data with informative observation times. Biometrika, 93, 763-775. Sun, J. & Wei, L. J. (2000). Regression analysis of panel count data with covariate-dependent observation and censoring times. Journal of the Royal Statistical Society, Series B, 62, 293-302. Sun, J., Tong, X., & He, X. (2007). Regression analysis of panel count data with dependent observation times. Biometrics, 63, 1053-1059. 2006; 93 1990 2005; 100 1993; 88 2002; 89 2008; 9 2000; 62 2007 1988; 83 1993 2007; 63 2009; 15 2001; 96 1995; 5 2005; 24 Sun J. (e_1_2_9_12_1) 1995; 5 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_8_1 e_1_2_9_7_1 Sun J. (e_1_2_9_11_1) 1993; 88 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_3_1 e_1_2_9_2_1 Thall P. F. (e_1_2_9_16_1) 1988; 83 e_1_2_9_9_1 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_17_1 Cook R. J. (e_1_2_9_4_1) 2007 |
| References_xml | – reference: He, X., Tong, X., Sun, J., & Cook, R. J. (2008). Regression analysis of multivariate panel count data. Biostatistics, 9, 234-248. – reference: Sun, J. & Kalbfleisch, J. D. (1995). Estimation of the mean function of point processes based on panel count data. Statistica Sinica, 5, 279-290. – reference: Sun, J., Tong, X., & He, X. (2007). Regression analysis of panel count data with dependent observation times. Biometrics, 63, 1053-1059. – reference: Andersen, P. K., Borgan, O., Gill, R. D., & Keiding, N. (1993). Statistical Models Based on Counting Processes, Springer-Verlag, New York. – reference: Lin, D. Y., Wei, L. J., & Ying, Z. (2001). Semiparametric transformation models for point processes. Journal of the American Statistical Association, 96, 620-628. – reference: Thall, P. F. & Lachin, J. M. (1988). Analysis of recurrent events: Nonparametric methods for random-interval count data. 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| SubjectTerms | Cancer Chemoprevention Computer simulation Consistent estimators Counting Counting processes Estimation bias Inference Information relevance Mathematical models MSC 2010: Primary 62N02 multivariate data analysis panel count data Panels Parameter estimation Point estimators Preventive medicine Recurrent Recurrent events Regression Regression analysis secondary 62G05 Simulation Skin cancer Skin cancers Squamous cell carcinoma Statistical bias Statistics Studies Transformation transformation models Transformations |
| Title | Semiparametric transformation models for multivariate panel count data with dependent observation process |
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